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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