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Native apps are known for their high performance, seamless integration with device features, and superior user experience compared to hybrid or web apps. But even the most well-designed native app can fail if it isn’t thoroughly tested. Bugs, compatibility issues, or performance lags can lead to poor reviews and user drop-off.

In this article, we’ll walk businesses through the purpose and methodologies of native app testing, explore different types of tests, and outline the key criteria to look for in a trusted native app testing partner.

By the end, companies will gain the insights needed to manage external testing teams with confidence and drive better app outcomes.

Now, let’s start!

What Is Native App Testing?

Native app testing is the process of evaluating the functionality, performance, and user experience of an app that is specifically developed for a particular operating system, such as iOS or Android, to make sure it functions correctly and delivers a high-quality user experience on its intended platforms. These apps are referred to as “native” because they are designed to take full advantage of the features and capabilities of a specific OS.

Definition of native app testing

The purpose of native app testing is to determine whether native applications work correctly on the platform for which they are intended, evaluating their functionality, performance, usability, and security.

Through robust testing, this minimizes the risk of critical issues and enhances the app’s success in a competitive app market.

Key Types of Native App Testing

Key types of native app testing

Unit testing

• Purpose: Verifies that individual functions or components of the application work correctly in isolation.
• Why it matters: Detecting and fixing issues at the unit level helps reduce downstream bugs and improves code stability early in the development cycle.

Integration testing

• Purpose: Checks how different modules of the app work together – like APIs, databases, and front-end components.
•  Why it matters: It helps identify communication issues between components, preventing system failures that can disrupt core user flows.

UI/UX testing

• Purpose: Evaluates how the app looks and feels to users – layouts, buttons, animations, and screen responsiveness.
• Why it matters: A consistent and intuitive interface enhances user satisfaction and directly impacts adoption and retention rates.

Performance testing

• Purpose: Tests speed, responsiveness, and stability under different network conditions and device loads.
• Why it matters: Ensuring smooth performance minimizes app crashes and load delays, both of which are key factors in maintaining user engagement.

Security testing

• Purpose: Assesses how well the app protects sensitive data and resists unauthorized access or breaches.
• Why it matters: Addressing security gaps is essential to protect sensitive information, meet compliance requirements, and maintain user trust.

Usability testing

• Purpose: Gathers real feedback from users to identify friction points, confusing flows, or overlooked design flaws.
• Why it matters: Feedback from usability testing guides design improvements and ensures that the app aligns with user expectations and behaviors.

Learn more: Software application testing: Different types & how to do?

Choosing the Right Approach to Native App Testing: In-House, Outsourced, or Hybrid?

One of the most strategic decisions in native app development is determining how testing will be handled. The approach taken can significantly affect not only time-to-market but also product quality, development efficiency, and long-term scalability.

Choose the right approach to native app testing

In-house testing

In-house testing involves building a dedicated QA team within the organization. This approach offers deep integration between testers and developers, fostering immediate feedback loops and domain knowledge retention.

Maintaining in-house teams makes sense for enterprises or tech-first startups planning frequent updates and long-term support.

Best fit for:

• Companies developing complex or security-sensitive apps (e.g., fintech, healthcare) require strict control over IP and data.
• Organizations with established development and QA teams capable of building and maintaining internal infrastructure.
• Long-term products with frequent feature updates and the need for cross-functional collaboration between teams.

In-house testing

Challenges:

• High cost of QA talent acquisition and retention, particularly for senior test engineers with mobile expertise.
• Requires significant upfront investment in devices, testing labs, and automation tools.
• May face resource bottlenecks during high-demand development cycles unless teams are over-provisioned.

Outsourced testing

With outsourced testing, businesses partner with QA vendors to handle testing either partially or entirely.

This model not only reduces operational burden but also gives businesses quick access to experienced testers, broad device coverage, and advanced tools. In fact, 57% of executives cite cost reduction as the primary reason for outsourcing, particularly through staff augmentation for routine IT tasks.

Best fit for:

• Startups or SMEs lacking internal QA resources are seeking cost-effective access to mobile testing expertise.
• Projects that require short-term testing capacity or access to specialized skills like performance testing, accessibility, or localization.
• Businesses looking to accelerate time-to-market without sacrificing testing depth.

Challenges:

• Reduced visibility and control over daily test execution and issue resolution timelines.
• Coordination challenges due to time zone or cultural differences (especially in offshore models).
• Requires due diligence to ensure vendor quality, security compliance, and confidentiality (e.g., NDAs, secure environments).

Outsourced testing

Hybrid model

The hybrid approach for testing allows companies to retain strategic oversight while extending QA capabilities through external partners. In this setup, internal QA handles core feature testing and critical flows, while external teams take care of regression, performance, or multi-device testing.

Best fit for:

• Organizations that want to retain strategic control over core testing (e.g., test design, critical modules) while outsourcing repetitive or specialized tasks.
• Apps with variable testing workloads, such as cyclical releases or seasonal feature spikes.
• Companies scaling up who need to balance cost and flexibility without compromising on quality.

Challenges:

• Needs strong project management and alignment mechanisms to coordinate internal and external teams.
• Risk of inconsistent quality standards unless test plans, tools, and reporting are well integrated.
• May involve longer onboarding to align both sides on tools, workflows, and business logic.

Hybrid model

5 Must-Have Criteria for a Trusted Native App Testing Partner

While every business has its own unique needs, there are key qualities that any reliable native app testing partner should consistently deliver. Below, we break down the 5 essential criteria that an effective software testing partner must meet and explain why they matter.

Choose a trusted native app testing partner

Proven experience in native app testing

A testing partner’s experience should extend beyond general QA into deep, hands-on expertise in native mobile environments. Native app testing demands unique familiarity with OS-level APIs, device hardware integration, and platform-specific performance constraints, whether it’s iOS/Android for mobile or Windows/macOS for desktop.

• For mobile, this means understanding how apps behave under different OS versions, permission models, battery usage constraints, and device-specific behaviors (e.g., Samsung vs. Pixel).
• For desktop, experience with native frameworks like Win32, Cocoa, or Swift is critical, especially for apps relying on GPU usage, file system access, or local caching.

Businesses should see case studies or proof points in your industry or use case, such as finance, healthcare, or e-commerce, where reliability, compliance, or UX is critical.

Certifications like ISTQB, ASTQB-Mobile, or Google Developer Certifications reinforce credibility, especially when combined with real-world results .

Robust infrastructure and real device access

A trusted testing partner must offer access to a wide range of real devices and system environments that reflect the business’s actual user base across both mobile and desktop platforms. This includes varying operating systems, screen sizes, hardware specs, and network conditions. Unlike limited simulations, testing on real devices ensures accurate performance insights and reduces post-launch issues.

Security, compliance, and confidentiality

Given the sensitive nature of app data, the native app testing partner must adhere to strict security standards and compliance frameworks (e.g., ISO 27001, SOC 2, GDPR).

More than just certification, this means implementing security-conscious testing environments that prevent data leaks, applying techniques like data masking or anonymization during production-like tests, and enforcing strict protocols such as signed NDAs, role-based access, and secure handling of test assets and code.

It’s also important to note that native desktop apps often interact more deeply with a system’s file structure or network stack than mobile apps do, which increases the surface area for security vulnerabilities.

Communication and collaboration practices

Clear, consistent communication is essential when working with an external testing partner. Businesses should expect regular updates on progress, test results, and issues so they can stay informed and make timely decisions. The partner should follow a structured process for planning, executing, and retesting and be responsive when priorities shift.

They also need to work smoothly within companies’ existing tools and workflows, whether that’s Jira for tracking or Slack for quick updates. Good collaboration helps avoid delays, improves visibility, and keeps your product moving forward efficiently.

Scalability and business alignment

An effective testing partner must offer the ability to scale resources in line with evolving product demands, whether ramping up for major releases or optimizing during low-activity phases. Flexible scaling guarantees efficient use of time and budget without compromising test coverage.

Equally important is the partner’s alignment with broader business objectives. Testing processes should reflect the development pace, release cadence, and quality benchmarks of the product. A well-aligned partner contributes not only to immediate project goals but also to long-term product success and market readiness.

Best Practices for Managing An External Native App Testing Team

For businesses exploring outsourced native app testing, effective team management is key to turning that investment into measurable outcomes. The 5 practices below help establish alignment, reduce friction, and unlock real value from the partnership.

Manage an external native app testing team

Define clear expectations from the start

A productive partnership begins with a clearly defined scope of work. Outline key performance indicators (KPIs), testing coverage objectives, timelines, and preferred communication channels from the outset.

Make sure the external testing team understands the product’s business goals, user profiles, and high-risk areas, whether it’s data sensitivity, user load, or platform-specific edge cases. Early alignment helps eliminate confusion, reduces the risk of missed expectations, and makes it easier to track progress against measurable outcomes.

Assign a dedicated point of contact

Appointing a liaison on both sides helps reduce miscommunication and speeds up decision-making. This role is responsible for managing test feedback loops, flagging blockers, and facilitating coordination across internal and external teams.

Integrate with development workflows

Embedding QA professionals within Agile teams enhances collaboration and accelerates issue resolution. When testers are involved from the outset, they can identify defects earlier, reducing costly rework and ensuring development stays on track.

In today’s multi-platform environment, where apps must perform reliably across operating systems, devices, and browsers, integrating QA into Agile sprints transforms compatibility testing into a continuous effort. Rather than treating it as a final-stage checklist, teams can proactively detect and resolve issues such as layout breaks on specific devices or OS-related performance lags.

Maintain consistent communication and reporting

Regular updates between the internal team and the external testing partner help avoid misunderstandings and keep projects on track. Weekly syncs or sprint reviews ensure that testing progress, bug status, and priorities are clearly understood.

Use structured reports and dashboards to show key metrics like test coverage, defect severity, and retesting status. As a result, businesses get to assess product quality quickly without wading through technical detail.

Connecting the external team to tools already in use, such as Jira, Slack, or Microsoft Teams, helps keep communication smooth. Such integration improves collaboration and speeds up release cycles.

Foster a long-term partnership mindset

Onboard the external testing team with the same thoroughness as internal teams. Provide access to product documentation, user personas, and business goals. When testers understand the broader context, they can identify issues that impact user experience and business outcomes more effectively. This strategic partnership fosters a proactive approach to quality, leading to more robust and user-centric products.

Check out the comprehensive test plan template for the upcoming projects.

How Long Does It Take To Thoroughly Test A Native App?

Thoroughly testing a native mobile application is a multifaceted endeavor. Timelines vary significantly based on:

• App complexity (simple MVP vs. feature-rich platform)
• Platforms supported (iOS, Android, or both)
• Manual vs. automation mix
• Number of devices and testing cycles

How long does it take to test a native app?

For a basic native app, such as a content viewer or utility tool with limited interactivity, end-to-end testing might take between 1 and 2 weeks, focusing primarily on functionality, UI, and device compatibility.

However, most business-grade applications – those involving user authentication, server integration, data input/output, or performance-sensitive features – typically require from 3 to 6 weeks of testing effort.

For feature-rich or enterprise-level native apps, particularly those that involve real-time updates, background processes, or complex data transactions, testing can stretch from 6 to 10 weeks or more.

This is especially true when multi-platform coverage (iOS, Android, desktop) and a wide range of devices and OS versions are required. Native apps on mobile often need to account for fragmented hardware ecosystems, while native desktop apps may require deeper testing of system-level access, file handling, or offline modes.

Ultimately, the real question is not just “how long,” but how early and how strategically QA is integrated. Investing upfront in test strategy, automation, and risk-based prioritization often results in faster releases and lower post-launch costs, making the testing timeline not just a cost center but a business enabler.

FAQs About Native App Testing

1. What is native app testing, and how is it different from web or hybrid testing?

Native app testing focuses on apps built specifically for a platform (iOS, Android, Windows) using platform-native code. These apps interact more directly with device hardware and OS features, so testing must cover areas like performance, battery usage, offline behavior, and hardware integration. In contrast, web and hybrid apps run through browsers or webviews and don’t require the same depth of device-level testing.

2. How do I know if outsourcing native app testing is right for my business?

Outsourcing is a good choice when internal QA resources are limited or when there’s a need for broader device coverage, faster turnaround, or specialized skills like security or localization testing. It helps reduce time-to-market while controlling costs, especially during scaling or high-volume release cycles.

3. How much does it cost to outsource native app testing?

While specific figures for outsourcing native app testing are not universally standardized, industry insights suggest that software testing expenses typically account for 15% to 25% of the total project budget. For instance, if the total budget for developing a native app is estimated at $100,000, the testing phase could reasonably account for $15,000 to $25,000 of that budget. This range encompasses various testing activities, including functional, performance, security, and compatibility testing.

Final Thoughts on Native App Testing 

By understanding what native app testing entails, weighing the pros and cons of different approaches, and applying best practices when working with external testing teams, businesses can make smart decisions. More importantly, companies will be better equipped to decide if outsourcing is the right path and how to do it in a way that maximizes efficiency.

Ready to get started? 

LQA’s professionals are standing by to help make application testing a snap, with the know-how businesses can rely on to go from ideation to app store.

With a team of experts and proven software testing services, we help you accelerate delivery, ensure quality, and get more value from your testing efforts.

Contact us today to get the ball rolling!

Did you know that 40% of testers are now using ChatGPT for test automation, and 39% of testing teams have reported efficiency gains through reduced manual effort and faster execution? These figures highlight the growing adoption of AI in software testing and its proven ability to improve productivity.

As businesses strive to accelerate development cycles while maintaining software quality, the demand for more efficient testing methods has risen substantially. This is where AI-driven testing tools come into play thanks to their capability to automate repetitive tasks, detect defects early, and improve test accuracy.

In this article, we’ll dive into the role of AI in software testing at length, from its use cases and advancements from manual software testing to how businesses can effectively implement AI-powered solutions.

What is AI in Software Testing?

As software systems become more complex, traditional testing methods are struggling to keep pace. A McKinsey study on embedded software in the automotive industry revealed that software complexity has quadrupled over the past decade. This rapid growth makes it increasingly challenging for testing teams to maintain software stability while keeping up with tight development timelines.

What is AI in Software Testing?

The adoption of artificial intelligence in software testing marks a significant shift in quality assurance. With the ability to utilize machine learning, natural language processing, and data analytics, AI-driven testing boosts precision, automates repetitive tasks, and even predicts defects before they escalate. Together, these innovations contribute to a more efficient and reliable testing process.

According to a survey by PractiTest, AI’s most notable benefits to software testing include improved test automation efficiency (45.6%) and the ability to generate realistic test data (34.7%). Additionally, AI is reshaping testing roles, with 23% of teams now overseeing AI-driven processes rather than executing manual tasks, while 27% report a reduced reliance on manual testing. However, AI’s ability to adapt to evolving software requirements (4.08%) and generate a broader range of test cases (18%) is still developing.

Benefits of AI in software testing

AI Software Testing vs Manual Software Testing

Traditional software testing follows a structured process known as the software testing life cycle (STLC), which comprises six main stages: requirement analysis, test planning, test case development, environment setup, test execution, and test cycle closure.

AI-powered testing operates within the same framework but introduces automation and intelligence to increase speed, accuracy, and efficiency. By integrating AI into the STLC, testing teams can achieve more precise results in less time. Here’s how AI transforms traditional STLC’s stages:

• Requirement analysis: AI evaluates stakeholder requirements and recommends a comprehensive test strategy.
• Test planning: AI creates a tailored test plan, focusing on areas with high-risk test cases and adapting to the organization’s unique needs.
• Test case development: AI generates, customizes, and self-heals test scripts, also providing synthetic test data as needed.
• Test cycle closure: AI assesses defects, forecasts trends, and automates the reporting process.

While AI brings significant advantages, manual testing remains irreplaceable in certain cases.

For a detailed look at the key differences between the two approaches, refer to the table below:

Learn more: Automation testing vs. manual testing: Which is the cost-effective solution for your firm?

Use Cases of AI in Software Testing

According to the State of Software Quality Report 2024, test case generation is the most common AI application in both manual and automated testing, followed closely by test data generation.

Still, AI and ML can advance software testing in many other ways. Below are 5 key areas where these two technologies can make the biggest impact:

Use Cases of AI in Software Testing

Automated test case generation

Just like how basic coding tasks that once required human effort can now be handled by AI, in software testing, AI-powered tools can generate test cases based on given requirements.

Traditionally, automation testers had to write test scripts manually using specific frameworks, which required both coding expertise and continuous maintenance. As the software evolved, outdated scripts often failed to recognize changes in source code, leading to inaccurate test results. This created a significant challenge for testers working in agile environments, where frequent updates and rapid iterations demand ongoing script modifications.

With generative AI in software testing, QA professionals can now provide simple language prompts to instruct the chatbot to create test scenarios tailored to specific requirements. AI algorithms will then analyze historical data, system behavior, and application interactions to produce comprehensive test cases.

Automated test data generation

In many cases, using real-world data for software testing is restricted due to compliance requirements and data privacy regulations. AI-driven synthetic test data generation addresses this challenge by creating realistic, customized datasets that mimic real-world conditions while maintaining data security.

AI can quickly generate test data tailored to an organization’s specific needs. For example, a global company may require test data reflecting different regions, including address formats, tax structures, and currency variations. By automating this process, AI not only eliminates the need for manual data creation but also boosts diversity in test scenarios.

Automated issue identification

AI-driven testing solutions use intricate algorithms and machine learning to detect, classify, and prioritize software defects autonomously. This accelerates issue identification and resolution, ultimately improving software quality through continuous improvement.

The process begins with AI analyzing multiple aspects of the software, such as behavior, performance metrics, and user interactions. By processing large volumes of data and recognizing historical patterns, AI can pinpoint anomalies or deviations from expected functionality. These insights help uncover potential defects that could compromise the software’s reliability.

One of AI’s major advantages is its ability to prioritize detected issues based on severity and impact. By categorizing problems into different levels of criticality, AI enables testing teams to focus on high-risk defects first. This strategic approach optimizes testing resources, reduces the likelihood of major failures in production, and enhances overall user satisfaction.

Continuous testing in DevOps and CI/CD

AI plays a vital role in streamlining testing within DevOps and continuous integration/ continuous deployment (CI/CD) environments.

Once AI is integrated with DevOps pipelines, testing becomes an ongoing process that is seamlessly triggered with each code change. This means every time a developer pushes new code, AI automatically initiates necessary tests. This process speeds up feedback loops, providing instant insights into the quality of new code and accelerating release cycles.

Generally, AI’s ability to automate test execution after each code update allows teams to release software updates more frequently and with greater confidence, improving time-to-market and product quality.

Test maintenance

Test maintenance, especially for web and user interface (UI) testing, can be a significant challenge. As web interfaces frequently change, test scripts often break when they can no longer locate elements due to code updates. This is particularly problematic when test scripts interact with web elements through locators (unique identifiers for buttons, links, images, etc.).

In traditional testing approaches, maintaining these test scripts can be time-consuming and resource-intensive. Artificial intelIigence brings a solution to this issue. When a test breaks due to a change in a web element’s locator, AI can automatically fetch the updated locator so that the test continues to run smoothly without requiring manual intervention.

If this process is automated, AI will considerably reduce the testing team’s maintenance workload and improve testing efficiency.

Visual testing

Visual testing has long been a challenge for software testers, especially when it comes to comparing how a user interface looks before and after a launch. Previously, human testers relied on their eyes to spot any visual differences. Yet, automation introduces complications – computers detect even the slightest pixel-level variations as visual bugs, even when these inconsistencies have no real impact on user experience.

AI-powered visual testing tools overcome these limitations by analyzing UI changes in context rather than rigidly comparing pixels. These tools can:

• Intelligently ignore irrelevant changes: AI learns which UI elements frequently update and excludes them from unnecessary bug reports.
• Maintain UI consistency across devices: AI compares images across multiple platforms and detects significant inconsistencies.
• Adapt to dynamic elements: AI understands layout and visual adjustments, making sure they enhance rather than disrupt user experience.

How to Use AI in Software Testing?

Intrigued to dive deeper to start integrating AI into your software testing processes? Find out below.

How to Use AI in Software Testing

Step 1. Identify areas where AI can improve software testing

Before incorporating AI into testing processes, decision-makers must pinpoint the testing areas that stand to benefit the most.

Here are a few ideas to get started with:

• Automated test case generation
• Automated test data generation
• Automated issue identification
• Continuous testing in DevOps and CI/CD
• Test maintenance
• Visual testing

Once these areas are identified, set clear objectives and success metrics for AI adoption. There are some common goals like increasing test coverage, test execution speed, and defect detection rates

Step 2. Choose between building from scratch or using proprietary AI tools

The next step is to choose whether to develop a custom AI solution or adopt a ready-made AI-powered testing tool.

The right choice depends on the organization’s resources, long-term strategy, and testing requirements.

Here’s a quick look at these 2 methods:

Build a custom AI system or use proprietary AI tools?

Build a custom AI system

In-house development allows for a personalized AI solution that meets specific business needs. However, this approach requires significant investment and expertise:

• High upfront costs: Needs a team of skilled AI engineers and data scientists.
• Longer development cycle: Takes more time to build compared to off-the-shelf AI tools.
• Ongoing maintenance: AI models need regular updates and retraining.

Case study: NVIDIA’s Hephaestus (HEPH)

The DriveOS team at NVIDIA developed Hephaestus, an internal generative AI framework to automate test generation. HEPH simplifies the design and implementation of integration and unit tests by using large language models for input analysis and code generation. This greatly reduces the time spent on creating test cases while boosting efficiency through context-aware testing.

How does HEPH work? 

HEPH takes in software requirements, software architecture documents (SWADs), interface control documents (ICDs), and test examples to generate test specifications and implementations for the given requirements.

HEPH technical architecture

The test generation workflow includes the following steps:

• Data preparation: Input documents such as SWADs and ICDs are indexed and stored in an embedding database, which is then used to query relevant information.
• Requirements extraction: Requirement details are retrieved from the requirement storage system (e.g., Jama). If the input requirements lack sufficient information for test generation, HEPH automatically connects to the storage service, locates the missing details, and downloads them.
• Data traceability: HEPH searches the embedding database to establish traceability between the input requirements and relevant SWAD and ICD fragments. This step creates a mapped connection between the requirements and corresponding software architecture components.
• Test specification generation: Using the verification steps from the requirements and the identified SWAD and ICD fragments, HEPH generates both positive and negative test specifications, delivering complete coverage of all aspects of the requirement.
• Test implementation generation: Using the ICD fragments and the generated test specifications, HEPH creates executable tests in C/C++.
• Test execution: The generated tests are compiled and executed, with coverage data collected. The HEPH agent then analyzes test results and produces additional tests to cover any missing cases.

Use proprietary AI tools

Rather than crafting a custom AI solution, many organizations opt for off-the-shelf AI automation tools, which come with pre-built capabilities like self-healing tests, AI-powered test generation, detailed reporting, visual and accessibility testing, LLM and chatbot testing, and automated test execution videos.

These tools prove to be beneficial in numerous aspects:

• Quick implementation: No need to develop AI models from the ground up.
• Lower maintenance: AI adapts automatically to application changes.
• Smooth integration: Works with existing test frameworks out of the box.

Some of the best QA automation tools powered by AI available today are Selenium, Code Intelligence, Functionize, Testsigma, Katalon Studio, Applitools, TestCraft, Testim, Mabl, Watir, TestRigor, and ACCELQ.

Each tool specializes in different areas of software testing, from functional and regression testing to performance and usability assessments. To choose the right tool, businesses should evaluate:

• Specific testing needs: Functional, performance, security, or accessibility testing.
• Integration & compatibility: Whether the tool aligns with current test frameworks.
• Scalability: Ability to handle growing testing demands.
• Ease of use & maintenance: Learning curve, automation efficiency, and long-term viability.

Also read: Top 10 trusted automation testing tools for your business

Step 3. Measure performance and refine

If a business chooses to develop an in-house AI testing tool, it must then be integrated into the existing test infrastructure for smooth workflows. Once incorporated, the next step is to track performance to assess its effectiveness and identify areas for improvement.

Here are 7 key performance metrics to monitor:

• Test execution coverage
• Test execution rate
• Defect density
• Test failure rate
• Defect leakage
• Defect resolution time
• Test efficiency

Learn more: Essential QA metrics with examples to navigate software success

Following that, companies need to use performance insights to refine their AI software testing tools or adjust their software testing strategies accordingly. Fine-tuning algorithms and reconfiguring workflows are some typical actions to take for optimal AI-driven testing results.

Challenges of AI in Software Testing

Challenges of AI in software testing

• Lack of quality data

AI models need large volumes of high-quality data to make accurate predictions and generate meaningful results.

But, in software testing, gathering sufficient and properly labeled data can be a huge challenge.

If the data used to train AI models is incomplete, inconsistent, or poorly structured, the AI tool may produce inaccurate results or fail to identify edge cases.

These data limitations can also hinder the AI’s ability to predict bugs effectively, resulting in missed defects or false positives.

The need for continuous data management and governance is crucial to make sure AI models can function at their full potential.

• Lack of transparency

One of the key challenges with advanced AI models, particularly deep learning systems, is their “black-box” nature. 

These models often do not provide clear explanations about how they arrive at specific conclusions or decisions. For example, testers may find it difficult to understand why an AI model flags a particular bug, prioritizes certain test cases, or chooses a specific path in test execution.

This lack of transparency can create trust issues among testing teams, who may hesitate to rely on AI-generated insights without clear explanations.

Plus, without transparency, it becomes difficult for teams to troubleshoot or fine-tune AI predictions, which may ultimately slow down the adoption of AI-driven testing.

• Integration bottlenecks

Integrating AI-based testing tools with existing testing frameworks and workflows can be a complex and time-consuming process.

Many organizations already use well-established DevOps pipelines, CI/CD workflows, and manual testing protocols.

Introducing AI tools into these processes often requires significant customization for smooth interaction with legacy systems.

In some cases, AI tools for testing may need to be completely reconfigured to function within a company’s existing infrastructure. This can lead to delays in deployment and require extra resources, especially in large, established organizations where systems are deeply entrenched.

As a result, businesses must carefully evaluate the compatibility of AI tools with their existing processes to minimize friction and maximize efficiency.

• Skill gaps

Another major challenge is the shortage of in-house expertise in AI and ML. Successful implementation of AI in testing software demands not only a basic understanding of AI principles but also advanced knowledge of data analysis, model training, and optimization.

Many traditional QA professionals may not have the skills necessary to configure, refine, or interpret AI models, making the integration of AI tools a steep learning curve for existing teams.

Companies may thus need to invest in training or hire specialists in AI and ML to bridge this skills gap.

Learn more: Develop an effective IT outsourcing strategy

• Regulatory and compliance concerns

Industries such as finance, healthcare, and aviation are governed by stringent regulations that impose strict rules on data security, privacy, and the transparency of automated systems.

AI models, particularly those used in testing, must be configured to adhere to these industry-specific standards.

For example, AI tools used in healthcare software testing must comply with HIPAA regulations to protect sensitive patient data.

These regulatory concerns can complicate AI adoption, as businesses may need to have their AI tools meet compliance standards before they can be deployed for testing.

• Ethical and bias concerns

AI models learn from historical data, which means they are vulnerable to biases present in that data.

If the data used to train AI models is skewed or unrepresentative, it can result in biased predictions or unfair test prioritization.

To mitigate these risks, it’s essential to regularly audit AI models and train them with diverse and representative data.

FAQs about AI in Software Testing

How is AI testing different from manual software testing?

AI testing outperforms manual testing in speed, accuracy, and scalability. While manual testing is time-consuming, prone to human errors, and limited in coverage, AI testing executes thousands of tests quickly with consistent results and broader coverage. AI testing also reduces long-term costs through automation, offering self-healing scripts that adapt to software changes. In contrast, manual testing requires frequent updates and more resources, making it less suitable for large-scale projects.

How is AI used in software testing?

AI is used in software testing to automate key processes such as test case generation, test data creation, and issue identification. It supports continuous testing in DevOps and CI/CD pipelines, delivering rapid feedback and smoother workflows. AI also helps maintain tests by automatically adapting to changes in the application and performs visual testing to detect UI inconsistencies. This leads to improved efficiency, faster execution, and higher accuracy in defect identification.

Will AI take over QA?

No, AI will not replace QA testers but will enhance their work. While AI can automate repetitive tasks, detect patterns, and even predict defects, software quality assurance goes beyond just running tests, it requires critical thinking, creativity, and contextual understanding, which are human strengths.

Ready to Take Software Testing to the Next Level with AI?

There is no doubt that AI has transformed software testing – from automated test cases and test data generation to continuous testing within DevOps and CI/CD pipelines.

Implementing AI in software testing starts with identifying key areas for improvement, then choosing between custom-built solutions or proprietary tools, and ends with continuously measuring performance against defined KPIs.

With that being said, successful software testing with AI isn’t without challenges. Issues like data quality, transparency, integration, and skill gaps can hinder progress. That’s why organizations must proactively address these obstacles for a smooth transition to AI-driven testing.

At LQA, our team of experienced testers combines well-established QA processes with innovative AI-infused capabilities. We use cutting-edge AI testing tools to seamlessly integrate intelligent automation into our systems, bringing unprecedented accuracy and operational efficiency.

Reach out to LQA today to empower your software testing strategy and drive quality to the next level.

The need for stringent quality control in software development is undeniable since software defects can disrupt interconnected systems and trigger major malfunctions, leading to significant financial losses and damaging a brand’s reputation.

Consider high-profile incidents such as Nissan’s recall of over 1 million vehicles due to a fault in airbag sensor software or the software glitch that led to the failure of a $1.2 billion military satellite launch. In fact, according to the Consortium for Information and Software Quality, poor software quality costs US’ companies over $2.08 trillion annually.

Despite the clear need for effective quality control, many organizations find its cost to be a major obstacle. Indeed, a global survey of IT executives reveals that over half of the respondents view software testing cost as their biggest challenge. No wonder, companies increasingly look for solutions to reduce these costs without sacrificing quality.

In this article, we’ll discuss software testing cost in detail, from its key drivers and estimated amounts to effective ways to cut expenses wisely.

Let’s dive right in!

4 Common Cost Drivers In Software Testing

A 2019 survey of CIOs and senior technology professionals found that software testing can consume between 15% and 25% of a project’s budget, with the average cost hovering around 23%.

So, what drives these substantial costs in software testing? Read on to find out.

4 Common Cost Drivers In Software Testing

Project complexity

First and foremost, the complexity of a software project is a key determinant of testing costs.

Clearly, simple projects may require only minimal testing, whereas complex, multifaceted applications demand more extensive testing efforts. This is due to the fact that complex projects usually feature intricate codebases, numerous integration points, and a wide range of functionalities.

Testing methodology

The chosen testing methodology also plays a big role in defining testing costs.

Various methodologies, such as functional testing, non-functional testing, manual, and automated testing, carry different cost implications.

Automated testing, while efficient, requires an upfront investment in tools and scripting but can save time and resources in the long run since it can quickly and accurately execute repetitive test cases.

On the other hand, manual testing might be more cost-effective for smaller projects with limited testing requirements, yet may still incur ongoing expenses.

Dig deeper: Automation testing vs. manual testing: Which is the cost-effective solution for your firm?

Testing team

The testing team’s type and size are also big cost factors. This includes choosing between an in-house and outsourced team, as well as considering the number and expertise of the company’s testing professionals.

An in-house team requires budgeting for salaries, benefits, and training to ensure they have the necessary skills and expertise. Alternatively, outsourcing to third-party providers or working with freelance testers can reduce fixed labor costs but may introduce additional considerations like contract fees and potential language or time zone differences.

Learn more: 6 reasons to choose software testing outsourcing

Regarding team size and skills, obviously, larger teams or those with more experienced testers demand higher costs compared to smaller teams or those with less experienced staff.

Testing tools and infrastructure

Another factor that significantly contributes to the overall cost of software testing is testing tools and infrastructure.

Tools such as test management software, test automation frameworks, and performance testing tools come with their own expenses, from software licenses, training, and ongoing maintenance, to support fees.

For further insights, consider these resources:

• Mobile application testing tools: choosing the right solution
• Top 10 trusted automation testing tools for your business
• 5 simple steps to opt for best-fitted automation testing tool

As for testing infrastructure, it refers to the environment a company establishes to perform its quality assurance (QA) work efficiently. This includes hardware, virtual machines, and cloud services, all of which add up to the overall QA budget.

8 Key Elements That Increase Software Testing Expenses

Even with a well-planned budget, unexpected costs might still emerge, greatly increasing the expenses of software testing.

Below are 8 major elements that may cause a company’s testing expenses to rise:

8 Key Elements That Increase Software Testing Expenses

• Rewriting programs: When errors and bugs are detected in software, the code units containing these issues need to be rewritten. This process can extend both the time and cost associated with software testing.
• System recovery: Failures during testing or software bugs can result in substantial expenditures related to system recovery. This includes restoring system functionality, troubleshooting issues, and minimizing downtime.
• Error resolution: The process of identifying and resolving bugs, which often requires specialized resources, extensive testing, and iterative problem-solving, can add new costs to the testing budget.
• Data re-entry: Inaccuracies found during testing often necessitate data re-entry, further consuming time and resources.
• Operational downtime: System failures and errors can disrupt operational efficiency, leading to downtime that causes additional costs for troubleshooting and repairs.
• Strategic analysis sessions: Strategic analysis meetings are necessary for evaluating testing strategies and making informed decisions. However, these sessions also contribute to overall testing costs through personnel, time, and resource expenditures.
• Error tracing: Difficulty in pinpointing the root cause of software issues can lengthen testing efforts and inflate costs. This involves tracing errors back to their source, investigating dependencies, and implementing solutions accordingly.
• Iterative testing: Ensuring that bug fixes do not introduce new issues often requires multiple testing rounds, known as iterative testing. Each iteration extends the testing timeline and budget as testers verify fixes and guarantee overall system stability.

How Much Does Software Testing Cost?

So, what’s the cost of software testing in the total development cost exactly?

It comes as no surprise that there’s no fixed cost of software testing since it varies based on lots of factors outlined above.

But here’s a quick breakdown of software testing cost estimation, based on location, testing type, and testing role:

• Cost estimation of QA testers based on location

Learn more: Top 10 software testing companies in Vietnam in 2022

• QA tester cost estimation based on type of testing

• QA tester cost estimation based on their role

How To Reduce Software Testing Costs?

Since many companies are questioning how to reduce the cost of software testing, we’ve compiled a list of top 8 practical best practices to help minimize these costs without compromising quality and results. Check them out below!

How To Reduce Software Testing Costs?

Embrace early and frequent testing

Testing should be an ongoing task throughout the development phase, not just at the project’s end.

Early and frequent testing helps companies detect and resolve bugs efficiently before they escalate into serious issues later on. Plus, post-release bugs are more detrimental and costly to fix, so addressing them early helps maintain code quality and control expenses.

Prioritize test automation

Test automation utilizes specialized software to execute test cases automatically, reducing the reliance on manual testing.

In fact, according to Venture Beat, 97% of software companies have already employed some level of automated testing to streamline repetitive, time-consuming QA tasks.

Although implementing test automation involves initial costs for tool selection, script development, and training, it ultimately leads to significant time and cost savings in the long term, particularly in projects requiring frequent updates or regression testing.

Learn more: Benefits of test automation: Efficiency, accuracy, speed, and ROI

Apply test-driven development

Test-driven development (TDD) refers to writing unit tests before coding. This proactive approach helps identify and address functionality issues early in the development process.

TDD offers several benefits, including cleaner code refactoring, stronger documentation, less debugging rework, improved code readability, and better architecture. Collectively, these advantages help reduce costs and enhance efficiency.

Consider risk-based testing

Risk-based testing prioritizes testing activities based on the risk of failure and the importance of each function.

By focusing on high-risk areas, this approach simplifies test planning and preparation according to the possibility of risks, which not only improves productivity but also makes the testing process more cost-effective.

Implement continuous testing and DevOps

DevOps focuses on combining development and operations, with testing embedded throughout the software development life cycle (SDLC).

When integrating testing into the DevOps pipeline like that, businesses can automate and execute tests continuously as new code is developed and integrated, thereby minimizing the need for expensive post-development testing phases.

Use modern tools for UI testing

Automating visual regression testing with modern, low-code solutions is an effective approach for UI testing.

These tools harness advanced image comparison, analyze and verify document object model (DOM) structure, on-page elements, and handle timeouts automatically. Thus, they allow for rapid UI tests – often in under five minutes – without requiring extensive coding.

In the long run, this practice saves considerable resources, reduces communication gaps among developers, analysts, testers, and enhances the development process’ overall efficiency.

Account for hidden costs

Despite efforts to manage and reduce software testing expenses, unexpected hidden costs can still arise.

For instance, software products with unique functionalities often require specialized testing tools and techniques. In such instances, QA teams may need to acquire new tools or learn specific methodologies, which can incur additional expenses.

Infrastructure costs can also contribute to hidden costs, including fees for paid and open-source software used in automated testing, as well as charges for cloud services, databases, and servers.

Furthermore, updates to testing tools might cause issues with existing code, necessitating extra time and resources from QA engineers.

Outsource software testers

For companies lacking the necessary personnel, skills, time, or resources for effective in-house testing, outsourcing is a viable alternative.

Outsourcing enables access to a broader pool of skilled testers, specialized expertise, and cost efficiencies, particularly in regions with lower labor costs, such as Vietnam.

However, it’s important for businesses to carefully evaluate potential outsourcing partners, establish clear communication channels, and define service-level agreements (SLAs) to ensure the quality of testing services.

For guidance on selecting the right software testing outsourcing partner, check out our resources on the subject:

• Strategic guide to choosing the right software QA company
• Software QA outsourcing: Dos and don’ts

At LQA – Lotus Quality Assurance, we offer a wide range of testing services, from software and hardware integration testing, mobile application testing, automation testing, web application testing, to embedded software testing and quality assurance consultation. Our tailored testing models are designed to enhance software quality across various industries.

4 Main Categories of Software Testing Costs

Software testing expenses generally fall into four primary categories:

4 Main Categories of Software Testing Costs

• Prevention costs

Prevention costs refer to proactive investments aimed at avoiding defects in the software. These costs typically include training developers to create maintainable and testable code or hiring developers with these skills. Investing in prevention helps minimize the likelihood of defects occurring in the first place.

• Detection costs

Detection costs are related to developing and executing test cases, as well as setting up environments to identify bugs. This involves creating, running tests, and simulating real-world scenarios to uncover issues early. Investing in detection plays a big role in finding and addressing problems before they escalate, helping prevent more severe issues later on.

• Internal failure costs

These costs are incurred when defects are found and corrected before the product is delivered. They encompass the resources and efforts needed to debug, rework code, and conduct additional testing. While addressing bugs internally helps prevent issues from reaching end users, it still causes significant expenses.

• External failure costs

External failure costs arise when technical issues occur after the product has been delivered due to compromised quality. External failure costs can be substantial, covering customer support, warranty claims, product recalls, and potential damage to the company’s reputation.

In general, the cost of defects in software testing accounts for a major portion of the total testing expenses, even if no bugs are found. Ensuring these faults are addressed before product delivery is of great importance for saving time, reducing costs, and maintaining a company’s reputation. By carefully planning and evaluating testing activities across these categories, organizations can develop a robust testing strategy that ensures maximum confidence in the final product.

FAQs about Software Testing Cost

Is performing software testing necessary?

Absolutely! Software testing is essential for identifying and eliminating costly errors that could adversely affect both performance and user experience. Effective testing also covers security assessments to detect and address vulnerabilities, which prevents customer dissatisfaction, business loss, and damage to the brand’s reputation.

How to estimate the cost of software testing?

To estimate the cost of software testing, companies need to break down expenses into key categories for clearer budget allocation.

These categories typically include:

• Personnel costs: This covers the salaries, benefits, and training expenses for testing team members, including testers, test managers, and automation engineers.
• Infrastructure costs: These costs encompass hardware, software, and cloud services needed for testing activities, such as server hardware, virtual machines, test environments, and third-party services.
• Tooling costs: For smaller projects, open-source testing tools may suffice, while larger projects might require premium tool suites, leading to higher expenses.

How much time do software testers need to test software solutions?

The duration of software testing projects varies based on lots of factors, from project requirements, the software’s type and complexity, to features and functionalities included and the testing team’s size.

Final Thoughts about Software Testing Cost

Software testing is a pivotal phase in the SDLC, and understanding its costs can be complex without precise project requirements and a clearly defined scope. Once the technology stack and project scope are established, organizations can better estimate their software testing costs.

For effective software testing cost reduction, companies can explore several strategies. Some of them are implementing early and frequent testing, leveraging test automation, adopting risk-based testing, and integrating testing into the DevOps pipeline. Additionally, outsourcing testing can offer significant cost benefits.

At LQA, we provide comprehensive software testing solutions designed to be both high-quality and cost-effective. Rest assured that your software is free of bugs, user-friendly, secure, and ready for successful deployment.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Mail: [email protected]
• Fanpage: https://www.linkedin.com/company/lqa/

Agile software development adopts an incremental approach to building software, and agile testing methodology follows suit by incrementally testing features as they are developed. Despite agile’s widespread adoption—reportedly used by 71% of companies globally—many organizations, especially those in regulated industries needing formal documentation and traceability, still rely on waterfall or hybrid development models. Meanwhile, some teams are currently transitioning to agile methodologies.

No matter where your organization stands in the agile journey, this article aims to provide a comprehensive understanding of agile testing fundamentals, from definition, advantages, and life cycle, to effective strategy.

Without further ado, let’s dive right into it!

What is Agile Testing?

Agile testing is a form of software testing that follows agile software development principles. It emphasizes continuous testing throughout the software’s development life cycle (SDLC). Essentially, whenever there is an update to the software code, the agile testing team promptly verifies its functionality to ensure ongoing quality assurance.

What is Agile Testing

In traditional development, testing occurred separately after the coding phase.

In agile, however, testing is an ongoing process, positioning testers between product owners and developers. This arrangement creates a continuous feedback loop, aiding developers in refining their code.

Two key components of agile software testing are continuous integration and continuous delivery.

Continuous integration involves developers integrating their code changes into a shared repository multiple times a day. Meanwhile, continuous delivery ensures that any change passing all tests is automatically deployed to production.

The primary motivation for adopting agile methodology in software testing is its cost and time efficiency. By relying on regular feedback from end users, agile testing addresses a common issue where software teams might misinterpret features and develop solutions that do not meet user requirements. This approach ensures that the final product closely aligns with user needs and expectations.

Agile Testing Life Cycle       

The testing life cycle in agile operates in sync with the overall agile software development life cycle, focusing on continuous testing, collaboration, and enhancement.

Essentially, it comprises 5 key phases, with objectives outlined below:

Agile Testing Life Cycle

Test planning

• Initial preparation: At the outset of a project is agile test planning, with testers working closely with product owners, developers, and stakeholders to fully grasp project requirements and user stories.
• User story analysis: Testers examine user stories to define acceptance criteria and establish test scenarios, ensuring alignment with anticipated user behavior and business goals.
• Test strategy: Based on the analysis, testers devise a comprehensive test strategy that specifies test types (unit, integration, acceptance, etc.,), tools, and methodologies to be employed.
• Test estimation: For effective test planning, it’s necessary for your team to estimate testing efforts and resources required to successfully implement each sprint of the strategy.

Check out How to create a test plan: Components, steps and template for further details.

Daily scrums (stand-ups)

• Collaborative planning: Daily scrum meetings, also known as stand-ups, facilitate synchronized efforts between development and testing teams, enabling them to review progress and plan tasks collaboratively.
• Difficulty identification: Testers use stand-ups to raise testing obstacles, such as resource limitations and technical issues, that may impact sprint goals.
• Adaptation: Stand-ups provide an opportunity to adapt testing strategies based on changes in user stories or project priorities decided in the sprint planning meeting.

Release readiness

• Incremental testing: Agile encourages frequent releases of the product’s potentially shippable increments. Release readiness testing ensures each increment meets stringent quality standards and is deployment-ready.
• Regression testing: Prior to release, regression testing in agile is conducted to validate that new features and modifications do not adversely impact existing functionalities.
• User acceptance testing (UAT): Stakeholders engage in UAT to verify software compliance with business requirements and user expectations before final deployment.

Test agility review

• Continuous evaluation: This refers to regular review sessions throughout the agile testing life cycle to assess the agility of testing processes and their adaptability to evolving requirements.
• Quality assessment: Test agility reviews help gauge the effectiveness of test cases in identifying defects early in the development phase.

Learn more: Guide to 5 test case design techniques with examples

• Feedback incorporation: Stakeholder, customer, and team feedback is all integrated to refine testing approaches, aiming to enhance overall quality assurance practices.

Impact assessment

• Change management: Change management in agile involves frequent adaptations to requirements, scope, or priorities. The impact assessment examines how these changes impact existing test cases, scripts, and overall testing efforts.
• Risk analysis: Testers examine possible risks associated with changes to effectively prioritize testing tasks and minimize risks.
• Communication: Impact assessment necessitates clear communication among development, testing, and business teams to ensure everyone comprehends the implications of changes on project timelines and quality goals.

4 Essential Components of an Agile Testing Strategy

In traditional testing, the process heavily relies on comprehensive documentation.

However, the testing process in agile prioritizes software delivery over extensive documentation, allowing testers to adapt quickly to changing requirements.

Therefore, instead of detailing every activity, teams should develop a test strategy that outlines the overall approach, guidelines, and objectives.

While there is no one-size-fits-all formula due to varying team backgrounds and resources, here are 4 key elements that should be included in an agile testing strategy.

Essential Components of an Agile Testing Strategy

Documentation

The first and foremost element of an agile testing strategy is documentation.

The key task here is finding the right balance—providing enough detail to serve its purpose without overloading or missing important information.

Since testing in agile is iterative, quality assurance (QA) teams must create and update a test plan for each new feature and sprint.

Generally, the aim of this plan is to minimize unnecessary information while capturing essential details needed by stakeholders and testers to effectively execute the plan.

A one-page agile test plan template typically includes the following sections:

One-page agile test plan template

Sprint planning 

In agile testing, it’s crucial for a team to plan their work within time-boxed sprints.

Timeboxing helps define the maximum duration allocated for each sprint, creating a structured framework for iterative development.

Within Scrum – a common agile framework, a sprint typically lasts for one month or less, during which the team aims to achieve predefined sprint goals.

This time-bound approach sets a rhythm for consistent progress and adaptability, fostering a collaborative and responsive environment that aligns with agile principles.

Apart from sprint duration, during sprint planning, a few key things should be factored in:

• Test objectives based on user stories
• Test scope and timeline
• Test types, techniques, data, and environments

Test automation

Test automation is integral to agile testing as it enables teams to quickly keep pace with the rapid development cycles of agile methodology.

But, one important question arises: which tests should be automated first?

Below is a list of questions to help you prioritize better:

• Will the test be repeated?
• Is it a high-priority test or feature?
• Does the test need to run with multiple datasets or paths?
• Is it a regression or smoke test?
• Can it be automated with the existing tech stack?
• Is the area being tested prone to change?
• Can the tests be executed in parallel or only sequentially?
• How expensive or complicated is the required test architecture?

Deciding when to automate tests during sprints is another crucial question to ask. Basically, there are two main approaches:

• Concurrent execution: Automating tests alongside feature development ensures immediate availability of tests, facilitating early bug detection and prompt feedback.
• Alternating efforts: Automating tests in subsequent sprints following feature development allows developers to focus on new features without interruption but may delay the availability of agile automated testing.

The choice between these approaches should depend on your team dynamics, project timelines, feature complexity, team skill sets, and project requirements. In fact, agile teams may opt for one approach only or a hybrid based on project context and specific needs.

Dig deeper into automation testing:

• Top 8 benefits of test automation
• A comparison between manual testing and automation testing
• How to choose the right test automation framework?
• 10 leading automation testing companies worldwide

Risk management

Conducting thorough risk analysis before executing tests boosts the efficiency of agile testing, making sure that resources are allocated effectively and potential pitfalls are mitigated beforehand.

Essentially, tests with higher risk implications require greater attention, time, and effort from your QA team. Moreover, specific tests crucial to certain features must be prioritized during sprint planning.

Contact LQA for expert agile testing solutions

Agile Testing Quadrants Explained

The agile testing quadrant, developed by Brian Marick, is a framework that divides the agile testing methodology into four fundamental quadrants.

By categorizing tests into easily understood dimensions, the agile testing quadrant enables effective collaboration and clarity in the testing process, facilitating swift and high-quality product delivery.

At its heart, the framework categorizes tests along two dimensions:

• Tests that support programming or the team vs. tests that critique the product
• Tests that are technology-facing vs. tests that are business-facing

But first, here’s a quick explanation of these terms:

• Tests that support the team: These tests help the team build and modify the application confidently.
• Tests that critique the product: These tests identify shortcomings in the product or feature.
• Tests that are technology-facing: These are written from a developer’s perspective, using technical terms.
• Tests that are business-facing: These are written from a business perspective, using business terminology.

Agile Testing Quadrants Explained

Quadrant 1: Technology-facing tests that support the team

Quadrant 1 includes technology-driven tests performed to support the development team. These tests, primarily automated, focus on internal code quality and provide developers with rapid feedback.

Common tests in this quadrant are:

• Unit tests
• Integration/API tests
• Component tests

These tests are quick to execute, easy to maintain, and essential for Continuous Integration and Continuous Deployment (CI/CD) environments.

Some example frameworks and agile testing tools used in this quadrant are Junit, Nunit, Xunit, RestSharp, RestAssured, Jenkins, Visual Studio, Eclipse, etc.

Quadrant 1: Technology-facing tests that support the team

Quadrant 2: Business-facing tests that support the team

Quadrant 2 involves business-facing tests aimed at supporting the development team. It blends both automated and manual testing approaches, seeking to validate functionalities against specified business requirements.

Tests in Q2 include:

• Functional tests
• Examples
• Story tests
• Prototypes
• Simulations

Here, skilled testers collaborate closely with stakeholders and clients to ensure alignment with business goals.

Tools like BDD Cucumber, Specflow, Selenium, and Protractor can help facilitate the efficient execution of tests in this quadrant.

Quadrant 2: Business-facing tests that support the team

Quadrant 3: Business-facing tests that critique the product

Quadrant 3 comprises tests that assess the product from both a business and user acceptance perspective. These tests are crucial for verifying the application against user requirements and expectations.

Manual agile testing methods are predominantly used in this quadrant to conduct:

• Exploratory testing
• Scenario-based testing
• Usability testing
• User acceptance testing
• Demos and alpha/beta testing

Interestingly, during UAT, testers often collaborate directly with customers to guarantee the product meets user needs effectively.

Quadrant 3: Business-facing tests that critique the product

Quadrant 4: Technology-facing tests that critique the product

Quadrant 4 focuses on technology-driven tests that critique the product’s non-functional aspects, covering from performance, load, stress, scalability, and reliability to compatibility and security testing.

Automation tools to run such non-functional tests include Jmeter, Taurus, Blazemeter, BrowserStack, and OWASP ZAP.

All in all, these four quadrants serve as a flexible framework for your team to efficiently plan testing activities. However, it’s worth noting that there are no strict rules dictating the order in which quadrants should be applied and teams should feel free to adjust based on project requirements, priorities, and risks.

Quadrant 4: Technology-facing tests that critique the product

Advantages of Agile Testing

Agile testing offers a host of benefits that seamlessly integrate with the agile development methodology.

Advantages of Agile Testing

• Shorter release cycles

Unlike traditional development cycles, where products are released only after all phases are complete, agile testing integrates development and testing continuously. This approach ensures that products move swiftly from development to deployment, staying relevant in a rapidly evolving market.

• Higher quality end product

Agile testing enables teams to identify and fix defects early in the development process, reducing the likelihood of bugs making it to the final release.

• Improved operational efficiency

Agile testing eliminates idle time experienced in linear development models, where testers often wait for projects to reach the testing phase. By parallelizing testing with development, agile maximizes productivity, enabling more tasks to be accomplished in less time.

• Enhanced end-user satisfaction

Agile testing prioritizes rapid delivery of solutions, meeting customer demands for timely releases. Continuous improvement cycles also ensure that applications evolve to better meet user expectations and enhance overall customer experience.

FAQs about Agile Testing

What is agile methodology in testing?

Agile testing is a form of software testing that follows agile software development principles. It emphasizes continuous testing throughout the software’s development lifecycle. Essentially, whenever there is an update to the software code, the testing team promptly verifies its functionality to ensure ongoing quality assurance.

What are primary principles of agile testing?

When implementing agile testing, teams must uphold several core principles as follows:

• Continuous feedback
• Customer satisfaction
• Open communication
• Simplicity
• Adaptability
• Collaboration

What are some common types of testing in agile?

Five of the most widely adopted agile testing methodologies in current practice are:

• Test-driven development
• Acceptance test-driven development
• Behavior-driven development
• Exploratory testing
• Session-based testing

What are key testing metrics in agile?

Agile testing metrics help gauge the quality and effectiveness of testing efforts. Here are some of the most important metrics to consider:

• Test coverage
• Defect density
• Test execution progress
• Test execution efficiency
• Cycle time
• Defect turnaround time
• Customer satisfaction
• Agile test velocity
• Escaped defects

Final Thoughts about Agile Testing

Agile testing aligns closely with agile software development principles, embracing continuous testing throughout the software lifecycle. It enhances product quality and enables shorter release cycles, fostering customer satisfaction through reliable, frequent releases.

While strategies may vary based on team backgrounds and resources, 4 essential elements that should guide agile testing strategies are documentation, sprint planning, test automation, and risk management.

Also, applying the agile testing quadrants framework can further streamline your team’s implementation.

At LTS Group, we boast a robust track record in agile testing—from mobile and web applications to embedded software and automation testing. Our expertise is validated by international certifications such as ISTQB, PMP, and ISO, underscoring our commitment to excellence in software testing.

Should you have any projects in need of agile testing services, drop LQA a line now!

Contact LQA for expert agile testing solutions

Understanding your strengths, vulnerabilities, and where your team should allocate time is crucial in the realm of cybersecurity. However, determining these factors and prioritizing tasks can be challenging. So, conducting penetration testing is a highly effective approach to gaining clarity.

Penetration testing is a solid basis for any security team. It excels at pinpointing what to focus on and proposing initiatives for your team’s future endeavors. So, what exactly is pen testing, and why is it so important? The following article will provide further insights.

What is Penetration Testing?

A penetration test (or pentest), is a sanctioned simulation of an attack carried out on a computer system to assess its security. Penetration testers employ identical tools, methods, and procedures as actual attackers to discover and illustrate the detrimental effects of system vulnerabilities.

These tests typically simulate diverse attack types that pose potential risks to a business. They evaluate the system’s ability to resist attacks from authorized and unauthorized sources, as well as various system roles. With appropriate parameters, a pen test can delve into any system facet.

Why is Penetration Testing Important?

A penetration test holds a strong significance in ensuring network security. This testing methodology enables businesses to accomplish the following objectives:

• Uncover security vulnerabilities preemptively, beating hackers to the punch.
• Identify gaps in information security compliance.
• Assess the response time of the information security team, gauging how quickly  to detect breaches and minimize the impact.
• Understand the potential real-world consequences of a data breach or cyber attack.
• Obtain actionable guidance for remediation purposes.

Penetration testing empowers security experts to methodically assess the security of multi-tier network architectures, custom applications, web services, and other IT components.

Penatration testing services and tools provide swift visibility into high-risk areas, letting businesses build security budgeting.

Comprehensive testing of an organization’s entire IT system, a web app or a mobile app is crucial for safeguarding critical data from cyber hackers and enhancing the IT department’s responsiveness during potential attacks.

5 Phases of Penetration Testing

Penetration testers replicate the tactics employed by determined adversaries. At LQA, we adhere to a comprehensive plan that encompasses the following penetration testing process:

Phase 1 – Estimation

In the first pen testing process, we need to understand the exact number of items in scope, such as

• HTTP requests in web application and API,
• screens & main functions in Android / iOS application,
• server and network devices,
• IP addresses in systems.

Then, we build a plan based on the function severity. The ranking on a scale of A to S will be used as a criterion for customers to select items to test from the estimate list.

Phase 2 – Preparation

In this phase, we need to prepare some things before testing, including:

• Web application information: Site name, host, system, cloud services, penetration testing type (remote, onsite), period time, environment testing.
• Access restriction: Restricting access with IP address, basic authentication, we also need some special configured access settings.
• Account information: Multiple permission settings, multiple accounts (username and password).
• Various process: Assessment of functions associated with various processes and external systems.
• Validation: Confirm other important information before testing.

Phase 3 – Penetration testing

In this phase, LQA’s testing team will:

• Schedule penetration testing
• Implement manual and automated testing
• Analyze and evaluate detected vulnerability
• Analyze and evaluate the case of threats and impacts when the vulnerability is exploited

Phase 4 – Report

In the report phase, we will:

• Send daily quick report for high-risk vulnerability detected.
• Write a summary and technical report, then deliver the final report.

Phase 5 – Re-testing

In the last phase, LQA’s testing team will re-test the vulnerabilities after remediating programs.

After completing a successful pen-test, an ethical hacker collaborates with the target organization’s information security team to share their findings.

Typically, these findings are categorized with severity ratings, enabling the prioritization of remediation efforts. Issues with the highest ratings are addressed first to ensure effective resolution.

A business uses these findings as a foundation for conducting additional investigations, assessments, and remediation to enhance its security posture.

At this stage, decision-makers and stakeholders become actively involved, and the IT or security team establishes deadlines to ensure prompt resolution of all identified security issues.

Pen Testing Approaches

Penetration testing includes a trio of primary approaches, each equipping pen testers with specific levels of information required to execute their attacks effectively.

• White box testing: In white box testing, the customer furnishes comprehensive system information, including accounts at various access levels. This ensures that the testing expert can band encompass system’s functionalities.
• Black box testing: Black box penetration testing is a form of behavioral and functional testing in which testers are intentionally kept unaware of the system’s inner workings. Organizations commonly engage ethical hackers for black box testing, as it simulates real-world attacks and provides insights into the system’s vulnerabilities.
• Gray box testing: Gray box testing combines white box and black box testing techniques. Testers are granted limited knowledge of the system, including low-level credentials, logical flow charts, and network maps. The primary objective of gray box testing is to identify potential code and functionality issues in the system.

>> Read more:

Best software testing methods to ensure top-quality applications

What Should Good Penetration Testing Include?

To ensure a robust the pen test engagement, business should conduct a thorough assessment of an organization’s attack surface.

This assessment aims to identify all conceivable entry points into the network, encompassing unsecured ports, unpatched vulnerabilities, misconfigured systems, and weak passwords.

By addressing these critical aspects, organizations can fortify their defenses against potential security breaches.

After the identification of potential entry points, the penetration tester proceeds to exploit them to gain network access. Once inside, pentesters meticulously examine the network for sensitive information, including customer data, financial records, and proprietary company secrets.

Furthermore, the tester endeavors to escalate privileges to obtain complete control over the network.

How Often Should Pen Tests Be Performed?

The frequency of conducting penetration testing varies based on several factors, yet most security experts advise performing it at least annually. This regular assessment aids in the detection of emerging vulnerabilities, including zero-day threats, ensuring proactive mitigation measures can be promptly implemented.

When planning the schedule for penetration testing, organizations should focus on the following key considerations:

• Cyber-attack risks: Organizations with increased exposure to potential financial and reputational damage, should prioritize regular security testing to proactively prevent cyber-attacks.
• Budget: The frequency of pen testing should align with the available budget and its flexibility. Larger companies may have the resources to conduct annual tests, while smaller businesses might opt for biennial assessments due to budget constraints.
• Regulatory requirements: Certain industries, such as banking and healthcare, have specific regulations mandating regular penetration testing. Compliance with these regulations should guide the frequency and timing of security assessments in those organizations.

Apart from regular scheduled penetration testing, organizations should also consider conducting security tests in response to the following aspects:

• Incorporating new network infrastructure or appliances into the network.
• Implementing upgrades to existing applications and equipment.
• Installing security patches.
• Establishing new office locations.
• Modifying end-user policies.

What are The Best Penetration Testing Tools?

Penetration testers employ a diverse range of tools to execute reconnaissance, identify vulnerabilities, and streamline essential aspects of the penetration testing process. Here are several widely used tools:

• Specialized operating systems: Penetration testers rely on specialized operating systems tailored to penetration testing and ethical hacking. 

Among these, Kali Linux stands out as the preferred choice. This open-source Linux distribution comes equipped with an array of built-in pen testing tools, including Nmap, Wireshark, and Metasploit.

• Credential-cracking tools: In the pursuit of uncovering passwords, penetration testers leverage credential-cracking tools. These software applications employ various techniques such as encryption-breaking or launching brute-force attacks.

By using bots or scripts, these tools systematically generate and test potential passwords until a successful match is found. Prominent examples encompass Medusa, Hydra, Hashcat, and John the Ripper.

• Port scanners: These tools enable pen testers to remotely examine devices for open and accessible ports, which can serve as potential entry points into a network. While Nmap remains the most popular port scanner, other commonly used options include Masscan and ZMap.
• Vulnerability scanners: These scanning tools are designed to identify known vulnerabilities in systems, enabling pen testers to swiftly pinpoint potential weaknesses and entryways into a target. Notable examples of vulnerability scanners include Nessus, Core Impact, and Netsparker.

Web vulnerability scanners include a specialized category of tools within the broader realm of vulnerability scanning. These scanners specifically evaluate web applications and websites to identify potential vulnerabilities. Notable examples in this domain include Burp Suite and OWASP’s Zed Attack Proxy (ZAP).

• Packet analyzers: Also referred to as packet sniffers, empower penetration testers to analyze network traffic by capturing and examining individual packets.

These tools provide insights into the origin, destination, and, in some cases, content of transmitted data. Prominent packet analyzers include Wireshark and tcpdump, widely recognized for their effectiveness in this domain.

• Metasploit: On the other hand, serves as a comprehensive penetration testing framework encompassing a multitude of functionalities. Its most significant attribute lies in its ability to automate cyber attacks.

Equipped with a comprehensive library of prewritten exploit codes and payloads, Metasploit empowers penetration testers to select an exploit, assign a payload to deliver to the target system, and delegate the remaining tasks to the framework itself.

Penetration Testing Case Study

Below are two outstanding LQA’s penetration testing case studies you can refer to:

SaaS penetration testing

Overview

The product is a SaaS service software system that uses the Microsoft Azure cloud based on business management.

Its features aim at the user experience and business development system for small and medium enterprises.

Project information

• Country: USA
• Domain: ERP
• Framework: .NET, Vue
• Tools Involved: Burp Suite Professional

What we did

Our objective was to assess the security of the web applications by conducting a thorough penetration test aligned with the OWASP Top 10. This helped us identify and mitigate vulnerabilities to enhance the security posture.

Findings

• Privilege Escalation
• Account Takeover
• Stored XSS
• File Upload Vulnerabilities
• Information Leakage

Achievements

We found 12 vulnerabilities, fixed 100% of severe issues, and did 1400 APIs tested.

Dental clinic management system penetration testing

Overview

Our client has a dental clinic management system to make appointments, bookings, check exam results, invoicing, etc.

However, their system, which was built a decade ago on outdated PHP, lacked optimized performance, sustainability, and security. So, they needed an experienced vendor to upgrade their technology stack to ensure easier maintenance and future development.

Project information

• Country: France
• Domain: Healthcare
• Framework: NodeJS, React
• Tools Involved: Burp Suite Professional

What we did

Based on our client’s requirements, we needed to assess the security of the web applications by conducting a thorough penetration test aligned with the OWASP Top 10. This helped to identify and mitigate vulnerabilities to enhance its security posture.

Findings

• SQL Injection
• Access Control Issues
• Weak Authentication Mechanism
• Information Leakage

Achievements

We found 8 vulnerabilities, fixed 100% severe issues, and did 390 APIs tested.

FAQ

How often should I run a penetration testing?

The optimal frequency of conducting penetration tests varies for each company, contingent upon factors such as the nature of its operations and its appeal to potential attackers.

In the case of highly sensitive activities, you should conduct penetration tests regularly, ideally several times per year. This approach ensures that the latest attack methods are thoroughly tested and safeguards against emerging threats.

For activities of lower sensitivity, you should perform a penetration test for each new version release or whenever significant features are added. This targeted approach focuses on assessing the security of specific updates or additions, thereby maintaining an adequate level of protection.

By tailoring the frequency of penetration tests to the unique characteristics and risk profile of each company, organizations can proactively address potential vulnerabilities and bolster their overall security posture.

I don’t have sensitive data, why would I be attacked?

No website is immune to cyberattacks, even those that may not possess sensitive data.

Hackers can have varied motivations, ranging from honing their skills and knowledge, to exploiting compromised servers for hosting malicious websites, generating profits, or even simply seeking amusement.

Among the most frequently targeted websites are those built on the WordPress platform. These sites often face automated attacks on a massive scale, targeting tens of thousands of websites.

The victims of such attacks are not specifically singled out, but rather fall victim to the widespread and indiscriminate nature of these automated campaigns.

How much does a pentest cost?

The required time and budget for testing depend on the scope and level of thoroughness desired.

If comprehensive and exhaustive testing is sought, it is natural if you expect a longer duration and, consequently, a higher financial investment.

You can contact LQA to have further discussion and detailed quotation.

What is the most important step in a penetration testing?

The estimation phase holds significant importance in a penetration test as it serves as the foundation for gathering crucial information about the target. This stage is particularly critical since having a comprehensive understanding of the target significantly simplifies the gaining access process.

What are the risks of penetration testing?

Improperly executed penetration tests can potentially result in significant damage, leading to adverse consequences. For instance, servers may experience crashes, essential data might be corrupted or compromised, and the overall aftermath could be a criminal hack.

>> Read more:

• How to create a test plan? components, steps and template
• Cybersecurity testing: definition, different types, and comprehensive guide
• Top 10 software testing companies in the world in 2023
• How to choose an automation testing services provider

Conclusion

In light of the continuously advancing and sophisticated nature of cyberattacks, we can’t overstate the significance of regular penetration testing in organizations. These tests play a vital role in identifying vulnerabilities, patching security loopholes, and validating the effectiveness of cyber controls.

By conducting pen testing methodology, organizations adopt a proactive approach to fortifying their infrastructure, software applications, and even their personnel against potential threats.

This proactive stance motivates the development of robust and continuous security measures that can adapt to the ever-changing cyber threat landscape, ensuring the organization remains resilient in the face of evolving challenges.

Leveraging the expertise of LQA, companies can establish a comprehensive defense against both recognized and unforeseen threats. By enlisting their support, you can proactively prevent, identify, and mitigate potential risks.

If you are eager to implement penetration testing, we encourage you to reach out to LQA. Contact us today for further discussion!

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Mail: [email protected]
• Fanpage: https://www.linkedin.com/company/lqa/

In the ever-evolving landscape of technology, application testing & quality assurance stands as crucial pillars for the success of any software product.

This article delves into the fundamentals of application testing, including its definition, various testing types, and how to test a software application.

We aim to provide a comprehensive guide that will assist you in understanding and optimizing your application testing process, ensuring the delivery of high-quality software products. Let’s get cracking!

What is Software Application Testing?

Software application testing involves using testing scripts, tools, or frameworks to detect bugs, errors, and issues in software applications.

It is a crucial phase in every software development life cycle (SDLC), helping to identify and resolve issues early on, ensuring application quality, and avoiding costly damage.

What is Software Application Testing?

According to CISQ, poor software cost the U.S. economy $2.08 trillion in 2020 alone. VentureBeat also reported that developers spend 20% of their time fixing bugs.

The costs of software bugs extend beyond the direct financial expenses that a software developer must make to fix the bugs. They lead to productivity loss due to worker downtime, disruptions, and delays. Additionally, they can harm a company’s reputation, indicating a lack of product quality to clients.

Moreover, bugs can introduce security risks, leading to cyberattacks, data breaches, and financial theft.

For instance, Starbucks was forced to close about 60% of its stores in the U.S. and Canada, due to a software bug in its POS system. In 1994, a China Airlines Airbus A300 crashed due to a software error, resulting in the loss of 264 lives.

These statistics and examples emphasize the importance of application testing. However, implementing an effective QA process requires essential steps and a comprehensive testing plan.

Software Application Testing Process: How to Test a Software Application?

A thorough software testing process requires well-defined stages. Here are the key steps:

Software Application Testing Process

Requirement analysis

During this initial phase, the testing team gathers and analyzes the testing requirements to understand the scope and objectives of the testing process.

Clear test objectives are defined based on this analysis, aligning the testing efforts with the overall project goals. 

This step is crucial for customizing the software testing lifecycle (STLC) and determining the appropriate testing approaches.

Test planning

After analyzing requirements, the next step is to determine the test plan strategy. Resources allocation, software testing tools, test environment, test limitations, and the testing timeline are determined during this phase:

• Resource allocation: Determining the resources required for testing, including human resources, testing tools, and infrastructure.
• Test environment setup: Creating and configuring the test environment to mimic the production environment as closely as possible.
• Test limitations: Identifying any constraints or limitations that may impact testing, such as time, budget, or technical constraints.
• Testing timeline: Establishing a timeline for testing activities, including milestones and deadlines.
• QA metrics: Determining testing KPIs and expected results to ensure the effectiveness of the testing process.

Check out the comprehensive test plan template for your upcoming project.

Test case design

In this phase, the testing team designs detailed test cases based on the identified test scenarios derived from the requirements. 

Test cases cover both positive and negative scenarios to ensure comprehensive testing coverage. The test case design phase also involves verifying and reviewing the test cases to ensure they accurately represent the desired software behavior.

For automated testing, test scripts are developed based on the test cases to automate the testing process.

Test execution

Test execution is where the actual testing of the software application takes place. Testers execute the predefined test cases, either manually or using automated testing tools, to validate the functionality of the software.

Input data and various conditions are simulated during this phase to assess how the software responds under different scenarios. Any defects encountered during testing are documented and reported for further analysis and resolution.

Delve deep into testing world:

• What is functional testing? Types and comprehensive guide
• What is non functional testing?

Test cycle closure and documentation

The final step involves closing the test cycle and documenting the testing process comprehensively.

A test completion matrix is prepared to summarize test coverage, execution status, and defect metrics. Test results are analyzed to identify trends, patterns, and areas for improvement in future testing cycles.

Comprehensive documentation of test results, defects, and testing artifacts is prepared for reference and software audit purposes. Conducting a lessons-learned session helps capture insights and best practices for optimizing future testing efforts.

Software Application Test Plan (STP)

A software application test plan is a comprehensive document that serves as a roadmap for the testing process of a software application or system. It outlines the approach, scope, resources, schedule, and activities required for effective testing throughout the software development lifecycle.

A well-crafted test plan is crucial for ensuring the success, reliability, and quality of a software product. It provides a detailed guide for the testing team, ensuring that testing activities are conducted systematically and thoroughly.

Software Application Test Plan (STP)

A standard test plan for application testing should define the following key features:

• Testing scope: Clearly define the boundaries and coverage of testing activities, including what functionalities, modules, or aspects of the application will be tested.
• Testing objective: Pinpoint the specific goals and objectives of the testing process, such as validating functionality, performance, security, or usability aspects.
• Testing approach: Outline the testing approach to be used, whether it’s manual testing, automated testing, or a combination of both. Define the test strategies, techniques, and methodologies to be employed.
• Testing schedule: Establish a detailed testing schedule that includes milestones, deadlines, and phases of testing (such as unit testing, integration testing, system testing, and user acceptance testing).
• Bug tracking and reporting: Define the process for tracking, managing, and reporting defects encountered during testing. Include details about bug severity levels, priority, resolution timelines, and communication channels for reporting issues.

In case you haven’t created a test plan before and desire to nail it the very first time, make a copy of our test plan template and tweak it until it meets your unique requirements.

By incorporating these key features into a test plan, organizations can ensure a structured and comprehensive approach to software application testing, leading to improved quality, reduced risks, and better overall software performance.

Before diving into the implementation of an application testing process, it is vital to grasp the different types of testing for a successful strategy. Application testing can be classified in various ways, encompassing methods, levels, techniques, and types. To gain a comprehensive and clear understanding of the application testing system, take a look at the infographic below.

Types of testing

Application Testing Methods

There are two primary application testing methods: Manual Testing and Automation Testing. Let’s explore the key differences between Manual Testing vs Automation Testing, and understand when to use each method effectively.

Manual testing

This testing method involves human QA engineers and testers manually interacting with the software app to evaluate its functions (from writing to executing test cases).

In manual testing, QA analysts carry out tests one by one in an individual manner to identify bugs, glitches, defects, and key feature issues before the software application’s launch. As part of this process, test cases and summary error reports are developed without any automation tools.

Manual testing is often implemented in the first stage of the SDLC to test individual features, run ad-hoc testing, and assess one-time testing scenarios. 

It is the most useful for exploratory testing, UI testing, and initial testing phases when detecting usability issues and user experience problems.

Automation testing

This testing method utilizes tools and test scripts to automate testing efforts. In other words, specified and customized tools are implemented in the automation testing process instead of solely manual forces.

It is efficient for repetitive tests, regression testing, and performance testing. Automation testing can accelerate testing cycles, improve accuracy, and ensure consistent test coverage across multiple environments.

Manual Test and Automation Test

Application Testing Techniques

Black box testing

Black box testing is a software application testing technique in which testers understand what the software product is supposed to do but are unaware of its internal code structure.

Black box testing can be used for both functional and non-functional testing at multiple levels of software tests, including unit, integration, system, and acceptance. Its primary goal is to assess the software’s functionality, identify mistakes, and guarantee that it satisfies specified requirements.

White box testing

White box testing, or structural or code-based testing, is the process of reviewing an application’s internal code and logic. 

Testers use code coverage metrics and path coverage strategies to ensure thorough testing of code branches and functionalities. It is effective for unit testing, integration testing, and code quality assessment.

Gray box testing

Gray box testing is a software application testing technique in which testers have a limited understanding of an application’s internal workings.

The principal goal of gray box testing is to combine the benefits of black box testing and white box testing to assess the software product from a user perspective and enhance its overall user acceptance. It is beneficial for integration testing, usability testing, and system testing.

Black box, Grey box and White box penetration testing differences

Application Testing Levels

Unit testing

Unit testing focuses on testing individual units or components of the software in isolation. It verifies the correctness of each unit’s behavior and functionality. Unit testing is most useful during development to detect and fix defects early in the coding phase.

Integration testing

Integration testing verifies the interactions and data flow between integrated modules or systems. It ensures that integrated components work together seamlessly. Integration testing is crucial during the integration phase of SDLC to identify interface issues and communication errors.

System testing

System testing evaluates the complete and fully integrated software product to validate its compliance with system specifications. It tests end-to-end functionality and assesses system behavior under various conditions. System testing is conducted before deployment to ensure the software meets user expectations and business requirements.

User acceptance testing

User acceptance testing (UAT) ensures that the software meets user expectations and business requirements. It involves real-world scenarios and is conducted by end-users or stakeholders.  Acceptance testing is often conducted in the final stages to ensure alignment with user expectations, business goals, and readiness for production deployment.

Software application testing levels

Types of Software Application Testing

Software application testing types

Functional test

Functional testing assesses whether the software application’s functions perform according to specified requirements. It verifies individual features, input/output behavior, and functional workflows.

Some common functional test types include:

• Compatibility testing: Verifies the software’s compatibility across different devices, operating systems, browsers, and network environments to ensure consistent performance and functionality.
• Performance testing: Assess the software’s responsiveness, scalability, stability, and resource utilization under varying workloads to ensure optimal performance and user satisfaction.
• Security testing: Identifies vulnerabilities, weaknesses, and potential security risks within the software to protect against unauthorized access, data breaches, and other security threats.
• GUI testing: Focuses on verifying the graphical user interface (GUI) elements, such as buttons, menus, screens, and interactions, to ensure visual consistency and proper functionality.

Non-functional test

Non-functional testing focuses on aspects such as security, usability, performance, scalability, and reliability of the software. It ensures that the software meets non-functional requirements and performs well under various conditions and loads.

Some common non-functional testing types implemented to ensure robust and user-friendly software include:

• API testing: Validates the functionality, reliability, and performance of application programming interfaces (APIs) to ensure seamless communication and data exchange between software components.
• Usability testing: Evaluates how user-friendly and intuitive the software interface is for end-users, focusing on ease of navigation, clarity of instructions, and overall user experience.
• Load testing: Assesses how the software performs under high volumes of user activity, determining its capacity to handle peak loads and identifying any performance bottlenecks.
• Localization testing: Verifies the software’s adaptability to different languages, regions, and cultural conventions, ensuring it functions correctly and appropriately in various local contexts.
• Accessibility testing: Ensures the software is usable by people with disabilities, checking compliance with accessibility standards and guidelines to provide an inclusive user experience.
• Penetration testing: Simulates cyberattacks on the software to identify security vulnerabilities, assessing its defenses against potential threats and breaches.

The ‘’in-between’’ testing types

In software development, several testing types bridge the gap between functional and non-functional testing, addressing aspects of both. These “in-between” testing types include:

• Regression testing: Checks for unintended impacts on existing functionalities after code changes or updates to ensure that new features or modifications do not introduce defects or break existing functionalities.
• Integration testing: Examines the interactions between integrated modules or components of the software, ensuring they work together as intended and correctly communicate with each other.
• System testing: Evaluates the complete and integrated software system to verify that it meets the specified requirements, checking overall functionality, performance, and reliability.
• User acceptance testing: Involves end-users testing the software in real-world scenarios to confirm it meets their needs and expectations, serving as the final validation before release.

Best Practices for Application Testing with LQA

With over 8 years of experience and being the pioneering independent software QA company in Vietnam, LQA is a standout entity within the LTS Group’s ecosystem, renowned for its expertise in IT quality and security assurance. We provide a complete range of application testing services, including web application testing, application security testing, mobile application testing, application penetration testing, etc.

LQA software quality assurance awards

With LQA, you can have the best practices in creating and implementing diverse types of application testing tailored to your business’s requirements. We stand out with:

• Expertise in industries: Our specialized experience, validated by awards like ISTQB, PMP, and ISO, ensures efficient and exceptional outcomes.
• Budget efficiency: Leveraging automation testing solutions, we deliver cost-effective results, benefitting from Vietnam’s low labor costs.
• TCoE compliance: Aligning with the Testing Center of Excellence (TCoE) framework optimizes QA processes, resources, and technologies for your project.
• Abundant IT talent: Our diverse pool of testers covers various specialties including Mobile and web app testing, Automation (Winform, Web UI, API), Performance, Pen Test, Automotive, Embedded IoT, and Game testing.
• Advanced technology: Leveraging cutting-edge testing devices, tools, and frameworks, our team guarantees the smooth operation of your software, delivering a flawless user experience and a competitive market advantage.

LQA robust software testing tools

LQA recognizes the crucial role of software quality testing in delivering top-tier software products. Our expertise and advanced testing methods enable businesses to attain robust, dependable, and high-performing software applications.

Frequently Asked Questions About Application Testing

What is application testing? 

Application testing refers to the process of evaluating software applications to ensure they meet specified requirements, perform as expected, and are free from defects or issues.

What does an application tester do?

An application tester is responsible for designing and executing test cases, identifying bugs or defects in software applications, documenting test results, and collaborating with developers to ensure issues are resolved.

Why is application testing required?

Application testing is required to verify that software functions correctly, meets user expectations, operates efficiently, and is reliable. It helps identify and address bugs, errors, and performance issues early in the development lifecycle, leading to higher-quality software.

What is computer application testing?

Computer application testing, also known as software application testing, is the process of testing software applications to validate their functionality, performance, security, usability, and other quality attributes on computer systems.

How to test a software application?

Testing a software application involves various stages such as requirement analysis, test planning, test case design, test execution, and test cycle closure. It includes manual testing where testers interact with the application and automated testing using testing tools and scripts to validate its behavior under different scenarios.

Final Thoughts About Software Application Testing

Quality assurance through rigorous application testing processes is the keystone that ensures software products meet user expectations, function flawlessly, and remain competitive in the market.

At LQA, we understand the paramount importance of software quality testing in delivering top-notch software products. Our testing services are designed to cater to diverse testing needs, including functional testing, performance testing, usability testing, and more. By leveraging our expertise and cutting-edge testing methodologies, businesses can achieve robust, reliable, and high-performing software applications.

Investing in thorough application testing is not just a best practice; it’s a strategic imperative. If you are looking for application testing experts to optimize your testing processes and ensure top-notch software quality, do not hesitate to contact our experts at LQA. Let us partner with you on your journey to delivering exceptional software solutions that exceed expectations.

Non functional testing and functional testing are both vital to ensure that your product operates as intended. Non functional testing examines aspects that go beyond functionality. It guarantees a superior level of product quality, performance, and usability, which can improve user satisfaction.

Within this blog post, we will provide a comprehensive definition of non functional testing. Furthermore, we will explore a range of examples showcasing non functional tests, shedding light on the specific areas they assess.

Additionally, we will guide you on the most effective approach to aligning non functional testing with your business objectives and user requirements, enabling your business to deliver a remarkable product that fulfills both functional and non functional testing expectations.

What Is Non Functional Testing?

Non functional testing is a critical software testing methodology that assesses an application’s non functional components, encompassing usability, performance, scalability, reliability, security, compatibility, and more.

→ Take a look at: LQA’s software testing services

Non functional testing focuses on ensuring the overall product quality rather than merely examining its features. You have to understand the significant impact that non functional testing has on a product.

In the realm of software development, non functional testing has equal importance to functional testing. Without it, a system may exhibit flawless performance in a controlled environment and encounter significant failures when confronted with real-world conditions.

Why Use Non Functional Testing?

Functional and non functional testing are both crucial for any software. Functional testing ensures the correct functioning of internal features, while non functional testing evaluates how well the software performs in the external environment.

Non functional testing plays a vital role in examining various aspects such as performance, stability, responsiveness, portability, and more. It involves assessing the software’s installation, setup, and execution.

By gathering measurements and metrics, non functional testing facilitates internal research and development efforts. It provides valuable insights into the software’s behavior and the technologies employed. Moreover, it helps mitigate production risks and reduces associated software costs.

Non Functional Testing Characteristics

The essential traits of non functional testing include:

• Non functional testing necessitates quantifiable metrics. Therefore, using subjective terms such as “good,” “better,” or “best” is not appropriate for this type of testing.
• During the initial stages of the requirement process, it may be challenging to ascertain precise figures.
• Giving priority to the requirements holds immense significance in non functional testing.

Non Functional Testing Types

The following are the prevalent types of non functional testing:

1. Performance testing

Performance testing aims to identify and address the factors that contribute to slow and constrained software performance. The software must exhibit fast response times, ensuring an efficient user experience.

To conduct effective performance testing, businesses should establish a well-defined and specific set of requirements regarding the desired speed. Without clear specifications, it’s hard to determine whether the test results indicate success or failure.

For instance, if 1000 users access an application together, the load time should not exceed 5 seconds.

Tools used: LoadRunner, Apache JMeter, WebLOAD.

2. Load testing

We use load testing to evaluate the system’s capacity to handle increasing concurrent users. It specifically assesses the system’s loading capability and its ability to cope with higher user loads. By simulating real-world scenarios, load testing helps identify potential bottlenecks and performance issues under heavy usage.

To gauge a website’s speed and performance, you can run a quick website speed test, which provides insights into the website’s speed scores. This helps measure the website’s responsiveness and overall user experience.

Tools used: Neoload, Load Multiplier.

3. Security testing

Security testing is employed to identify a software application’s vulnerabilities and weaknesses. This type of testing involves examining the system’s design and adopting the mindset of a potential attacker.

By scrutinizing the application’s code, and potential attack vectors, security testers can pinpoint areas where an attack is most likely to occur. This knowledge is then used to create targeted and effective test cases that assess the application’s resilience against potential security breaches.

Tools Used: ImmuniWeb, Vega, Wapiti

4. Portability testing

Portability testing focuses on assessing the software’s capability to operate seamlessly across multiple operating systems without encountering any bugs or compatibility issues.

Additionally, this testing also examines the software’s functionality when deployed on the same operating system but with different hardware configurations. By conducting portability testing, one can ensure that the software performs consistently and reliably across various environments, enhancing its usability and flexibility.

Tools Used: SQLMap.

5. Accountability testing

Accountability testing plays a crucial role in determining the correctness of system functionality. The primary objective is to ensure that each function in the system consistently produces the expected outcome for which it was designed.

If the system generates the desired results, it passes the accountability test; however, if it fails to do so, it indicates a potential flaw or malfunction in the system’s functionality.

By conducting thorough accountability testing, one can effectively assess and validate the system’s performance and its ability to meet the intended objectives.

Tools Used: Mentimeter.

6. Reliability testing

Reliability testing is based on the premise that the software system runs without errors within predefined parameters. It involves running the system for a specified duration and several processes to assess its reliability.

The reliability test is considered unsuccessful if the system fails under predetermined circumstances.

For instance, in the case of a website, all web pages and links should be dependable and function reliably. If the system exhibits issues or malfunctions, such as broken links or errors, during the reliability test, it indicates a failure to meet the expected reliability standards.

By conducting reliability testing, one can evaluate the system’s ability to consistently operate as intended and identify any potential weaknesses or areas for improvement in terms of reliability and error-free performance.

Tools Used: Test-retest, Inter-rater.

7. Efficiency testing

Efficiency testing examines the utilization of resources during a software system’s construction, assessing both the actual resources employed and the ones required. This type of testing aims to determine the efficiency and optimization of resource usage throughout the software development process.

By analyzing resource consumption, such as CPU usage, memory utilization, or network bandwidth, efficiency testing provides insights into the software system’s resource requirements and helps identify potential areas for improvement in resource allocation and utilization.

Tools Used: WebLOAD, LoadNinja.

8. Volume testing

Volume testing, also referred to as flood testing, is a type of software testing that entails subjecting the software to a substantial amount of data. Its purpose is to evaluate the system’s performance by increasing the volume of data in the database.

By simulating scenarios with a large and often excessive amount of data, volume testing helps assess the system’s ability to handle and process such data loads without compromising its performance or stability.

This type of testing ensures that the software can effectively manage and scale with growing data volumes, thus preventing any potential bottlenecks or performance issues.

Tools Used: HammerDB, JdbcSlim.

9. Recovery Testing

Recovery testing assesses an application’s resilience in recovering from crashes, hardware failures, and similar issues.

By intentionally breaking the software through simulated scenarios, recovery testing aids in identifying vulnerabilities and weaknesses in the recovery mechanisms. This type of testing helps make sure that the application can gracefully handle unexpected failures, quickly restore functionality, and minimize any potential data loss or system downtime.

Tools Used: Box Backup, Bacula.

10. Responsive testing

Responsive testing enables you to evaluate your design across a range of screen widths, providing a more authentic assessment of its adaptability rather than relying solely on predetermined screen sizes.

By utilizing specialized tools, you can test your website’s responsiveness by adjusting the screen width dynamically after entering the website’s URL.

This allows you to observe how your user interface adapts and adjusts in real-time to accommodate different screen sizes.

The primary objective of evaluating responsive websites is to ensure a seamless and friendly user experience across various digital devices. By conducting responsive testing, we can ensure that websites and applications deliver a smooth and consistent experience to users, regardless of the device they are using.

Tools Used: Responsinator, Screenfly, Google DevTools Device Mode.

11. Visual testing

One way to address issues is using visual testing (or visual UI testing). This type of testing focuses on validating whether the software user interface (UI) is displayed correctly to every user.

Visual tests meticulously examine each element on a web page to ensure they have the proper shape, size, and placement as intended. By comparing the application’s visible output to the expected design outcomes, visual testing helps identify “visual bugs” that may exist, separate from functional bugs that affect the software’s overall functionality.

In essence, visual testing plays a crucial role in detecting any discrepancies or issues related to a page or screen’s appearance and presentation.

Tools Used: Percy, PhantomCSS, FBSnapshotTestCase, Gemini, Needle (Uses Python).

→ Read more:

• Best Software Testing Methods to Ensure Top-quality Applications
• 5 Steps To Hire A Test Automation Engineer
• How to Choose an Automation Testing Services Provider

Non Functional Testing Parameters

Let’s delve into these parameters and examine them in detail:

• Security: The security parameter establishes the level of protection a system has against both intended and unintended attacks originating from internal or external sources. Security testing is conducted to assess and verify this protection.
• Reliability: The reliability parameter examines a system’s capability to perform its intended functions consistently, without any failures over a specific duration. Using reliability testing to evaluate and validate this ability.
• Survivability: The survivability parameter determines a product’s capacity to maintain its operation and recover from failures or disruptions. We use recovery testing to assess and validate this ability.
• Availability: The availability parameter determines the level of reliability and consistency a user can expect from a system and its functionalities during operation. We use stability testing to measure and evaluate this parameter.
• Usability: The usability parameter gauges the user’s ease of interaction with a product, including learning, operating, and input/output preparation. Usability testing is employed to evaluate this aspect and ensure optimal user experience.
• Scalability: Scalability assesses a system’s capability to adjust its performance in response to varying workloads without compromising its effectiveness. Scalability testing is used to evaluate this ability and ensure optimal performance.
• Interoperability: The interoperability parameter determines a system’s capacity to interface with other software systems smoothly. Interoperability testing is conducted to verify this ability and ensure smooth integration.
• Efficiency: Efficiency measures the software system’s ability to handle volume, capacity, and response time effectively.
• Flexibility: Flexibility refers to the application’s continuous operation across a wide range of hardware and software configurations. For instance, most applications have specific minimum RAM and CPU requirements to ensure proper functionality.
• Portability: Portability refers to the ease with which an application can transit from one hardware or software environment to another.
• Reusability: Reusability denotes a component or module in a software system that can be utilized in multiple applications.

Best Practices Of Non Functional Testing

To achieve effective non functional testing, you should take certain best practices into account. 

• Early engagement: Engage in non functional test activities starting from the early phases of the software development life cycle (SDLC). Collaborate closely with stakeholders, architects, and developers to comprehend non functional requirements and incorporate them into the system design.
• Well-defined goals: Establish precise and measurable objectives for non functional testing. Set clear targets for performance, security, usability, and other non functional aspects to guide the testing process and provide a basis for evaluation.
• Realistic test environment: Set up a test environment that resembles the production environment. Use representative hardware, software, network configurations, and data volumes to ensure accurate analysis of performance and behavior.
• Test automation: Employ test automation tools and frameworks to streamline and expedite non functional testing. Automation facilitates the simulation of user loads, generation of consistent test data, and execution of repetitive tasks, resulting in more efficient and dependable testing.

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• Monitoring and performance metrics: Implement robust monitoring mechanisms throughout testing to capture performance metrics such as response times, resource utilization, throughput, and error rates. These metrics provide valuable insights into system behavior, aid in identifying bottlenecks, and facilitate performance analysis.
• Risk-based testing: Prioritize non functional test cases based on risk analysis and their impact on business operations. Give attention to critical functionalities, high-risk areas, and cases that are likely to lead to performance degradation, security vulnerabilities, or usability issues.
• Continuous improvement: Foster a culture of ongoing improvement by leveraging insights from testing experiences and incorporating feedback into subsequent iterations. Capture lessons learned, update documentation, and refine testing strategies based on the knowledge gained during non functional testing.

These practices represent only a fraction of the existing methods for efficient non functional testing. By adhering to them, organizations can conduct effective non functional testing to ensure optimal performance, security, usability, and other non functional attributes of their software systems.

Examples Of Non Functional Testing

To gain a better understanding of this concept, let’s explore some examples of non functional testing across different types. The table below illustrates a range of non functional test cases specifically for web applications.

How To Align Non Functional Testing With Business Goals And User Needs?

Here are some valuable tips to seamlessly align non functional testing with your business objectives and user requirements.

Understand the context

Before commencing non functional testing, you should comprehend the project context, your intended audience, and your business goals.

What are your users’ and clients’ expectations and demands? What are your domain’s and environment’s risks and challenges? Which standards and regulations apply to your software?

Addressing these queries will assist in defining the scope, criteria, and priorities for your non functional testing.

Choose the right techniques

Non functional testing is not a one-size-fits-all approach. Depending on the context, different techniques and tools may be required to measure and evaluate the non functional aspects of your software.

For instance, we use load testing, stress testing, and endurance testing to assess system performance under varying levels of demand. Usability testing, accessibility testing, and user experience testing can be utilized to evaluate user satisfaction and convenience.

Security testing, penetration testing, and vulnerability testing can help identify and mitigate potential threats and breaches. Maintainability testing, portability testing, and compatibility testing ensure software adaptability and interoperability.

Align with functional testing

Non functional testing should not be treated as a standalone or separate activity from functional testing. Instead, it should be integrated and harmonized with functional testing throughout the software development life cycle.

This approach ensures the relevance, consistency, and comprehensiveness of nonfunctional testing while avoiding duplication, confusion, and conflicts with functional testing.

For instance, leveraging test automation allows for efficient and effective execution of both functional and non functional testing.

Additionally, incorporating non functional requirements and specifications into test cases and code through test-driven development, or behavior-driven development further enhances the integration of non functional aspects.

Communicate the results

Non functional testing goes beyond simply identifying and addressing defects. It also offers valuable insights and feedback to stakeholders and users. Therefore, you should communicate the results of non functional testing in a clear, concise, and persuasive manner.

You can apply various methods and formats to present and report non functional testing results, including graphs, charts, dashboards, metrics, or narratives. Additionally, different channels and platforms can be used to share and discuss these results, such as emails, meetings, demos, or blogs.

The key is to emphasize the benefits and impacts of non functional testing on business goals and user requirements.

Learn and improve

Non functional testing is not a one-off or stagnant endeavor. It’s an ongoing and dynamic process that necessitates continual learning and improvement. Regular and frequent monitoring and measurement of software performance and quality are essential.

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Furthermore, you should review and update non functional testing strategies and techniques in response to the evolving needs and expectations of stakeholders and users. You can apply range of sources and methods, such as surveys, interviews, reviews, or analytics to collect and analyze feedback and data.

Additionally, leveraging various tools and frameworks, such as DevOps, Agile, or Lean, can provide support and enhance non functional testing efforts.

Differences Between Functional And Non Functional Testing Requirements

Let take a quick look at some differences between nonfunctional testing and functional testing:

FAQ

What is functional vs non functional testing?

Functional testing ensures that the application works as intended. In contrast, non functional testing evaluates the application’s efficiency, performance, security, scalability, reliability, and portability.

What are non functional testing examples?

Non functional testing focuses on evaluating the non functional aspects of the product. To gain a clearer understanding, consider the following examples:

• Validate that the application’s dashboard loads within 5 seconds upon login.
• Ensure that email notifications are dispatched within 3 minutes.
• Verify that the application supports concurrent login by 500 users simultaneously.

What are the challenges of non functional testing?

Below are several risks related to non functional testing:

• Risk #1: Performance bottlenecks.
• Risk #2: Security vulnerabilities.
• Risk #3: Subpar user experience.
• Risk #4: Compatibility issues.
• Risk #5: Scalability challenges.

What will happen if non functional requirements are ignored?

Neglecting non functional requirements (NFRs) can significantly affect the adoption of the system, leading to various consequences.

These include the system’s inability to scale up to meet customer demands, sluggish performance resulting in unresponsiveness, security breaches compromising confidential data, and system unavailability during critical periods. Those directly impact business operations.

What is the main goal of non functional testing?

The objective of non functional testing is to enhance the usability, effectiveness, maintainability, and portability of the product. This testing process helps mitigate the manufacturing risk associated with the non functional aspects of the product.

Final Thoughts On Non Functional Testing

Non functional testing plays a crucial role in guaranteeing the overall quality and success of software systems. It extends beyond functional requirements and concentrates on pivotal aspects such as performance, security, usability, scalability, and reliability.

By conducting comprehensive non functional testing, organizations can effectively mitigate risks, elevate user satisfaction, adhere to industry standards, and optimize costs.

At LQA, we have the excellent expertise, specialized skills, and knowledge to conduct comprehensive assessments and evaluations of non-functional attributes.

Our team is highly proficient in utilizing specialized tools and techniques, enabling them to proactively identify and address potential issues.

With our proficiency in performance testing, security testing, usability testing, and compliance testing, we are adept at uncovering hidden problems, optimizing system performance, enhancing security measures, and ensuring a seamless user experience.

Our ultimate goal is to provide clients with high-quality software systems that meet performance expectations, prioritize user satisfaction, safeguard against security threats, and comply with industry standards.

If you are eager to improve the quality and reliability of your software systems, we encourage you to reach out to LQA. Contact us today to discuss your testing requirements and elevate your software to new heights.

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