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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.

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.

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:

Aspect	Manual testing	AI testing
Speed and efficiency	Time-consuming and needs significant human effort. Best for exploratory, usability, and ad-hoc testing.	Executes thousands of tests in parallel, reducing redundancy and optimizing efficiency. Learns and improves over time.
Accuracy and reliability	Prone to human errors, inconsistencies, and fatigue.	Provides consistent execution, eliminates human errors, and predicts defects using historical data.
Test coverage	Limited by time and resources. Suitable for real-world scenario assessments that automated tools might miss.	Expands test coverage significantly, identifying high-risk areas and executing thousands of test cases within minutes.
Cost and resource	Requires skilled testers, leading to high long-term costs. Labor-intensive for large projects. Best for small-scale applications.	Reduces long-term expenses by minimizing manual effort. AI-driven testing automation tools automate test creation and execution, running continuously.
Test maintenance	Needs frequent updates and manual adjustments for every software change, increasing maintenance costs.	Self-healing test scripts automatically adjust to evolving applications, reducing maintenance efforts.
Scalability	Difficult to scale across multiple platforms, demanding additional testers for large projects.	Easily scalable with cloud-based execution, supporting parallel tests across different devices and browsers. Ideal for large-scale enterprise applications.

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:

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.

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

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.

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

• 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.

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.

 

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:

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.

 

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.

 

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.

 

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.

 

 

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.

 

Types of Software Application Testing

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.

 

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 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.

→ Don’t miss: 10 BEST Automation Testing Companies Worldwide in 2023

• 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.

→ Read more:

• How to Create A Test Plan? Components, Steps and Template
• Benefits of Test Automation: Efficiency, Accuracy, Speed, and ROI
• A Comparison of Manual Testing vs. Automation Testing

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.

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

In today’s rapidly evolving digital landscape, safeguarding software integrity is a top priority. White box penetration testing is a crucial cornerstone in the proactive defense strategy against emerging cyber threats. This detailed testing approach offers a unique viewpoint, much like a hacker’s perspective from inside the system, enabling a thorough exploration of potential vulnerabilities deeply embedded within the software. 

As the digital world continues to expand and evolve, so do the sophisticated techniques of cyber attackers, white box penetration testing serves as a crucial tool in staying ahead of these threats by revealing weaknesses in the system’s core, allowing for proactive reinforcement of security measures.

Understanding the pivotal role of this method within software quality assurance is essential, as it not only identifies existing vulnerabilities but empowers organizations to proactively strengthen their software, fostering resilience against potential breaches and cyber-attacks.

 

What Is White Box Penetration Testing?

White box penetration testing definition, referred to as clear box or structural testing, is a technique that grants the tester access to the internal structure of the system to replicate a hacker’s actions and uncover potential vulnerabilities. This method provides a comprehensive understanding of the application, identifying all possible entry points into the system.

White box pentest is frequently employed to examine a system’s essential parts, particularly by companies that develop their software products, or integrate multiple applications. It is a method to evaluate a system’s security by assessing its capability to withstand various real-time attacks.

 

Benefits of White Box Penetration Testing

An efficient white box penetration test helps avoid the issues, errors, and oversights that can leave your businesses vulnerable to hackers. Let’s explore more benefits of white-box penetration testing:

• Comprehensive oversights of possible issues: White box penetration testing offers the most comprehensive analysis of internal and external vulnerabilities from the internal point of view, which is not available to typical attackers.
• Early detection: White box penetration testing is integrated into the early development stages, when there is no user interface, and even before the software application is available to users, which enables detecting the vulnerabilities at a very early stage.
• Extensive testing coverage: White box penetration testing can identify weaknesses in areas that are unreachable for black box testing, for instance, an app’s source code, design, and business logic.
• Precise identification of weaknesses: Since testers have detailed knowledge of the internal workings of the system, they can pinpoint specific weaknesses, potential security gaps, and flaws in the code logic. This level of detail often leads to more accurate identification of vulnerabilities.

Disadvantages of White Box Testing

Despite all the appealing advantages, white box penetration testing shows some drawbacks in certain situations:

• High programming language requirements: Implementing white-box penetration testing involves internal network testing, which requires the testers to be familiar with critical programming tasks, like performing port scanning, SQL injection, and common attacks. By this, they will have a better understanding of the potential access points.
• Limited real-world simulation: White box testing operates with complete knowledge of the system, which doesn’t accurately replicate real-world attack scenarios where attackers have limited or no knowledge. This approach might overlook vulnerabilities that would be apparent to external attackers working with less information.
• Risk of biased testing: Testers, armed with complete system details, might inadvertently focus on known weaknesses or areas they are more familiar with, potentially overlooking other vulnerabilities that could be exploited by attackers with different perspectives.

 

Black Box, Grey Box and White Box Penetration Testing Differences

Black box, grey box and white box testing are all types of penetration testing – the practice of testing a computer system, network, or web app to find issues, errors, and vulnerabilities that an attacker could exploit. 

 

To help you distinguish between black box, grey box and white box penetration testing, understand the benefits and limitations of each type, and when to apply it to get the best results, we have summarized it in the following comparison table:

Aspects	Black box penetration testing	Grey box penetration testing	White box penetration testing
Level of knowledge requirement	Require little or no knowledge of infrastructure and network	Require basic knowledge of the internal codebase, architecture, and infrastructure	Allow complete access to knowledge about the system’s infrastructure, codebase, and network
Level of programming language requirement	Require no syntactic knowledge of the programming language	Require a basic comprehension of the programming language	Require high and professional understanding of programming language
Standard techniques	Boundary value analysis, Graph-Based testing, Equivalence partitioning, etc	Regression testing, Pattern testing, Matrix testing, Orthogonal array testing, etc	Decision coverage, Path testing, Branch testing, Statement coverage, etc
Advantages	– Mimics real-world attacks – Provides an outsider’s perspective – Encourages creative problem-solving	– Balances realism and deeper insights – Enables access to some internal system knowledge – Optimize time and resources	– Understands thoroughly of the system’s internals – Delivers comprehensive coverage of system security – Pinpoints vulnerabilities in code and architecture
Disadvantages	– Limited insight into internal structures – Incomplete view of vulnerabilities – Possible overlook of certain critical vulnerabilities	– Restricted insight compared to White Box – Dependent on available information – Possible miss of certain system areas	– Time-consuming due to in-depth analysis – Costly due to skilled personnel and time- Prone to false positives if not done carefully
When to use	– Simulating external threats – Testing overall security posture – Assessing response to unknown attackers	– Balancing depth and efficiency – Targeted testing with some internal insights – Limited access but need for deeper insight	– Assessing specific system components – Analyzing code, architecture, and design – Identifying and fixing intricate flaws

 

The selection of Black Box, Grey Box, or White Box Penetration Testing depends on the level of internal knowledge required, the depth of the assessment needed, and the specific objectives of your security testing rpojects. It’s often beneficial to employ a combination of these methodologies for a comprehensive security assessment based on the unique needs of the system or software being evaluated.

 

White Box Penetration Testing Techniques

When it comes to software security testing, security testing white box techniques review source code (the internal structure of the software application) to detect gaps that can make an application vulnerable to cybersecurity threats.

One of the main goals of white box penetration testing is to cover the complete source code as extensively as possible. Three main types of techniques for use in white box penetration testing include Path coverage, Statement coverage, and Branch coverage.

Path coverage

This white box test methodology pays attention to all the paths. The path is a flow of execution that follows a set of instructions. The path coverage examines all possible paths of the software and ensures each path is traversed at least once. The path coverage is far more powerful than the branch coverage and is useful for testing complicated builds.

 

Statement coverage

Statement methodology checks if each functionality was tested one time. A statement indicates a functionality or set of actions for the application to decode depending on its programming language. 

An executable statement is when the statement is put together and transformed into an object code, which will subsequently execute the action it was designed for. It helps to uncover unused or missing statements and branches as well as leftover dead codes.

The statement coverage evaluates if each line of code is executed at least once and helps find unnecessary or missing lines.

 

Branch coverage

A branch is one of many execution paths that the code can take after processing a decision statement like an if statement. This method is to confirm that all branch codes are tested.

The branch coverage is tested to check whether all branches in a codebase are exercised by tests and no branch leads to abnormal behavior of the application. It maps the code into branches of conditional logic and ensures that all branches are covered by unit tests.

One should ascertain that all codes have been launched at least once.

 

Common White Box Penetration Testing Tools

Several common tools/libraries employed in white-box penetration testing include:

1. Metasploit: Penetration testers utilize Metasploit to create and authenticate exploit code before deploying it in real-world scenarios. It’s instrumental for network security testing or remote system intrusion.
2. Nmap: As an open-source network administration tool, Nmap monitors network connections and scans extensive networks, aiding in host and service auditing as well as intrusion detection. It offers packet-level and scan-level analysis and is freely available for download.
3. PyTest: Pytest, a comprehensive Python testing tool, facilitates writing more efficient programs, supporting test-driven development (TDD) and behavior-driven development (BDD).
4. NUnit: NUnit is an open-source unit testing framework beneficial for the .NET Framework and Mono, aiding in writing better code and reducing application bugs.
5. John the Ripper: This fast password cracker identifies weak Unix passwords and is compatible with various operating systems such as Unix, Windows, DOS, BeOS, and OpenVMS. John the Ripper supports multiple password hash types commonly found in Unix systems and other patches contributed by users.
6. Wireshark: Functioning as a network traffic analyzer, Wireshark enables monitoring and analyzing traffic within system networks. It is open-source and widely recognized as the foremost network analyzer globally, primarily used by network administrators and professionals to troubleshoot network and system performance issues and filter various network protocols.

The tools employed in white-box penetration testing are similar to those used in other penetration tests, but the methodology for employing these tools differs significantly.

Essential White Box Penetration Testing Steps

A process of software white box penetration testing comprises the following steps:

Source code review

The initial step is understanding the internal structure and functionality of a target software application. This crucial step requires a test engineer to review thoroughly the software’s source code, and understand clearly how it works in order to set the foundation for designing test cases that will help encounter security weaknesses.

 

Select the testing areas

After understanding completely the software’s internal structure and how it functions, the next step is determining the areas that need to be tested. 

As the test aims to encompass every potential scenario for running code systematically, it proves more effective to explore the numerous possibilities within a smaller area rather than a larger one, as the latter wouldn’t ensure the same comprehensive coverage.

Covering a vast area is feasible, yet it demands significant effort, resources, and labor for test coverage. Consequently, it’s not recommended to execute this extensive coverage only on demand. For instance, it becomes essential in situations where it’s crucial to safeguard every aspect of the system; in such cases, it would be deemed necessary.

 

Code & flowchart identification

This step adds a structured approach to the white box penetration testing by visually mapping the code execution process, facilitating a more organized and systematic analysis of the system’s functionalities.

• Identify potential code lines: Thoroughly examine the system and identify all possible code segments associated with the functionalities or aspects under test. This involves a comprehensive review of the codebase, focusing on critical areas that could be potential sources of vulnerabilities.
• Create a flow chart: Outline the flow of the identified code segments. Create a flow chart or diagram to represent the flow of code execution, including input points, processing stages, and output results.
• Output tracing: Document and trace the output of each code segment within the flow chart. This helps in understanding how inputs are processed and how outputs are generated, aiding in the identification of potential vulnerabilities and understanding the system’s behavior.

 

Design test cases

Designing test cases is a pivotal phase in white box penetration testing, involving the creation of detailed scenarios for every identified code segment and system functionality. 

Each test case outlines potential vulnerabilities, failure points, and specific testing procedures. It includes boundary testing, attack scenario simulations, and meticulous recording of testing outcomes to comprehensively evaluate the system’s security posture and ensure a systematic approach to identifying and addressing vulnerabilities.

 

Execute testing 

The execution phase in white box security testing involves putting the devised plans into action, rigorously conducting tests according to the outlined strategies, and repeatedly iterating through the testing process until all identified systems are thoroughly examined, leaving no vulnerabilities unchecked.

This phase includes comprehensive testing, meticulous documentation of findings, validation of vulnerabilities, and continual refinement of testing procedures to ensure the system’s robust security against potential threats.

 

Reporting 

Compile a detailed report that includes identified vulnerabilities, their potential impact, and recommendations for mitigation. This report should prioritize vulnerabilities based on their severity and guide how to address them.

 

Continuous improvement

Security is an ongoing process. Continuous monitoring, regular security assessments, and improvement in policies and practices are essential to maintain a robust security posture.

 

White Box Penetration Testing by LQA

Enhancing cybersecurity testing involves engaging a specialized security firm to assess your business’s vulnerabilities and deliver a detailed report with recommended solutions, a crucial step in preventing cyber attacks.

Having more than 7 years of experience, and as the pioneering independent software QA in Vietnam, LQA stands out as a prominent IT quality and security assurance firm, offering a complete range of penetration testing services to fortify businesses against security threats.

Alongside white box penetration testing services, LQA provides comprehensive software testing services including white box, black box, web application, mobile application, API, manual, and automation testing services.

At LQA, we maintain up-to-date expertise on the latest threats, attacks, and vulnerabilities, employing industry-leading tools to conduct comprehensive penetration tests.

Key features of LQA’s white box cyber security solution:

• Comprehensive software QA solutions covering consulting, planning, execution, and ongoing support.
• Ensured bug rate lower than 3% for devices, mobile, and web applications.
• Rapid delivery facilitated by a diverse pool of proficient testers.
• Optimal price-to-quality ratio, leveraging cost benefits and expertise of Vietnamese IT human resources.
• Tailored solutions based on industry-specific experience.
• Maximum security ensured through a Non-disclosure Agreement (NDA) and best security practices during database access.

Connect with LQA’s experts to safeguard your data and assets from potential hackers today!

 

Frequently Asked Questions about Haptic Feedback

1. What is white box penetration testing?

White box penetration testing is a comprehensive security assessment method where testers have complete access to the internal architecture, design, and system details of the target. In this approach, the tester possesses full knowledge of the system’s infrastructure, including source code, network diagrams, and system configurations.

2. What is a white box penetration testing example?

An example of a white box test could involve analyzing the source code of a web application to identify vulnerabilities. Testers would scrutinize the code, look for potential security flaws, and examine the database structure and application logic to uncover weaknesses in the system.

3. What are black box grey box and white box penetration testing?

Black box, grey box, and white box penetration testing are distinct approaches used in security assessments to evaluate the vulnerabilities of a system. Here are the brief definitions of each type of penetration testing:

• Black box penetration testing: A security testing method where testers have no prior knowledge of the system. They approach it as an external hacker would, without any internal information about the system’s architecture or design.
• Grey box penetration testing: A security testing method where testers have partial knowledge of the system, such as limited access or some details about the internal architecture. This approach combines elements of both white and black box testing.
• White box penetration testing: A security testing method where testers have complete access to the internal architecture, design, and system details of the target. Testers possess full knowledge of the system’s infrastructure, including source code, network diagrams, and system configurations.

4. What is the difference between black box and white box penetration testing?

The main difference between black box vs white box penetration testing lies in the level of information and access the testers have. White box testing involves complete access to the internal structure, code, and system design. On the other hand, black box testing operates without any knowledge of the internal system; testers approach it as an external attacker.

5. What is more costly black box or white box penetration testing?

Typically, white box penetration testing is more resource-intensive and thus can be more costly. It demands a higher level of expertise, time, and resources due to the need for in-depth knowledge of the system’s internal workings, including analysis and evaluation of code, architecture, and configurations.

6. What is the white box penetration testing methodology?

White box penetration testing is not just a single test but a methodology involving a structured and systematic approach. It involves various steps such as reconnaissance, scanning, vulnerability assessment, exploitation, and reporting. The white box security testing methodologies focus on a deep dive into the internal workings of a system to identify and mitigate potential vulnerabilities and security risks. White box testing is an essential part of a comprehensive security assessment, ensuring a thorough evaluation of system security from an insider’s perspective, and it plays a crucial role in strengthening the overall security posture of an organization’s infrastructure.

 

Final Thoughts About Whitebox Penetration Test

White box penetration testing serves as an effective method to strengthen software security. The level of complexity varies based on the application under assessment. Evaluating a small application that conducts straightforward operations is a swift process, often taking only a few minutes. However, larger applications necessitate significantly more time, ranging from days to weeks or even months.

Conducting these tests is crucial during the software development phase, both after its initial writing and following any subsequent modifications. Integrating white box penetration testing into your security strategy is pivotal, as it aids in preventing mistakes and oversights that could potentially expose your company to cyber threats.

If you are looking for experts in conducting white box testing for your IT environment or apps to check if they’re secure, don’t hesitate to contact LQA’s security testing team.

 

Automation Testing is a testing technique utilizing automated testing tools to implement tests on multiple platforms. This is considered an efficient software testing method coming with high accuracy and low labor consumption. Still, some obvious and hidden problems do exist behind.

Top 5 automation testing challenges that enterprises have to face:

• High initial investment cost
• High demand for necessary skills
• Complicated maintenance
• Complicated execution
• Difficulties in lab management

This article will dig into these 5 common challenges facing automation testing and solutions to minimize their effects on enterprises.

Top 5 Automation Testing Challenges

1. High initial investment cost

First, let’s take a closer look at the initial investment cost of automation testing. To estimate and calculate the Return of Investment, the first thing you should consider is the possible initial investment cost for an automation testing system, including:

• Cost for human resources
• Cost for automation tools

Cost for human resources

The automation testing process involves the utility of Automated Testing Tools and Automated Testing Engineers. These people are also called Software Development Engineers in Test. 

When comparing the Non-Technical Testers and those with Industrial Knowledge, the second ones are far more expensive. 

Also, the overall In-Demand positions for software testers are plummeting, specifically for automation testers, resulting in higher recruitment competitiveness and higher budgets for talent acquisition.

 

 

The dilemma of human resources lies upon the two forces, which are the Testing Engineers fluent in different coding languages and the Domain Expertise with non-technical knowledge and experience in coding. Whether the testers are onshore or offshore, the cost for those with coding skills is much higher than that of the non-technical testers.

To put it differently, The Non-technical Testers with knowledge of the industry are the trade-off for the Automated Testing Engineers.

Solutions: The problem of high cost for automation test engineers could be handled in two ways:

• Training current employees: This is a budget-friendly way to overcome challenges in automation testing. Still, it often takes many months for an Automated Testing Engineer to really hit it off. 
• Outsourcing automated testing engineers: To avoid spending months on training and coaching, many firms have chosen the solution of outsourcing automated testing engineers.

 

Cost for automation tools

There are two main types of automation testing tools: open-source and commercial testing tools. While the open-source testing frameworks, also called free testing tools (such as Selenium, Katalon, etc.) are free to access, the commercial ones require a payment based on licenses or the number of users.

Still, there are “hidden costs” no matter whether you’re using an open-source testing tool or a licensed one. As for the commercial framework, the payments are obviously the license and development costs. At the same time, free automation testing tools maybe not be enough for your business needs.

Solution: To reduce the cost of automation tools, you should first clarify your requirements and check if free tools can handle your needs. If not, go to a commercial solution that can benefit you the most in the long run.

 

2. Demand for high skills

The myth of automation testing is that it is always wrongly deemed as “simple”, “easy” or “quick”. In fact, the test execution including test design, writing test scripts, test maintenance and technical issue resolution, requires such high automation knowledge and solid grasp of automation tools that the salary range for automation testing engineers is very high.

Typically, automation testing engineers are required to fulfill the job requirements in terms of automation frameworks, prominent programming skills, and solid knowledge of the available automation tools. The strategic skillsets of identifying the appropriate frameworks, applying the right tools, and coordinating the testing process are vital for any automation testing engineer.

Solutions: Companies can weigh the pros and cons of in-house or outsourcing teams for automation testing. These necessary skills above can be acquired through either in-house training or automation testing vendors.

 

3. Complicated maintenance

As automation testing is the hot issue of quality assurance services, its maintenance is imperative for the overall efficiency of the testing process. Throughout the whole testing process, once a test case/script is written, it always necessitates maintenance, which is required every time the software application or features change. 

 

 

The scope of test maintenance varies in accordance with the complication level of the changes themselves. Whether it is a functional or non-functional feature update in the application, viable test cases are to be executed prior to release. As in the comparison of Automation Testing vs. Manual Testing, Automation Testing has different maintainability levels, entailing high programming skills.

Solutions:

• Modular test framework

By applying a modular framework for automated tests, the testing execution is divided into smaller pieces with different functions. Each function of the update is tested, making it easier for automation testing engineers to locate the code that needs updating.

• A separate test for each verification point

There might be a possibility that test developers of automation testing can create numerous verification points. However, the test scripts would hold the crux of complexity, making it difficult for anyone, other than the coder, to edit. With separate test for each verification point, it is easier for the team to update.

• Continuous Integration and Continuous Delivery

Continuous Integration and Continuous Delivery (CI/CD) are the methods in which the minor details/changes are well-attended. With these being applied, the development and testing process is faster and more efficient.

The implementation of CI/CD equals the robust reporting of test scripts and test results. If bugs are to be leaked into other environments, the CI/CD pipeline can help you with the testing process in identifying which part needs updating.

 

4. Complicated execution

During execution, automation scripts are run with input test data. Once execution is finished, detailed test reports will be available. From these reports, appropriate and viable changes and updates can be made.

Automation Testing Execution invokes some difficulties in:

• Test approach selection
• Automation testing tool selection
• Communication and Collaboration

 

High Demand in Test Approach Selection

An appropriate automation test approach plays a key role in the effective result of a project. 

At the management level, you certainly know what and how to make the test approach; however, to make this approach in test automation is another issue. 

• The first difficulty is making the long-run automation process associated with the lifespan of a product. For example, the average cycle of a desktop application is common from 12-18 months to over 15 years. Therefore, the test approach needs to be able to execute the whole process of the software’s life span. 
• Secondly, the test approach has to make sure that when products change or update, it is capable of identifying and keeping up with these changes without human intervention. Taking the example of a mobile application, the approach can’t be “one size fits all” because the user requirement rapidly changes.

Definitely, it is hard to address these difficulties on the test approach, facing the challenge of building an effective long-run-oriented framework at the beginning.

Solution: Identify the following features:

• Testing process
• Testing levels
• Testing types
• Automation tools applicable
• HR allocation with different roles and responsibilities

 

Diverse choices of automation testing tools

One of the automation testing challenges is to select the right testing tool among a variety of comprehensive test tools in the market. There are open-source and commercial tools, and there are various types within each category. Each tool is suitable for particular scenarios, such as Selenium is an open-source tool that requests more programming skills from testers.

Particularly, the right tool has to match many factors such as the long-term orientation of the project, framework, output of the project, the requirement of clients, and the skill of the tester team. So, if you pick the wrong or inappropriate tool, the whole process can be failed from the start. Indeed, open-source tools often require a higher level of coding skill than commercial tools.

Solution:

Our expert testers recommend the following steps to choose tools:

1. Defining a set of tool requirements criteria
2. Reviewing the chosen tools
3. Conducting a trial test with the tools
4. Making the final decision whether you use these tools or not?

 

Barriers in communication and collaboration

In comparison with manual testing and development, automated tests actually require more collaboration. Once the misunderstanding from the start is disregarded or neglected, the process can be messy.

From the beginning, the must-have is good interaction between the delivery team and customer to analyze and understand completely the input and output of the project. 

When it comes to the test strategy, the tester team needs to communicate with project managers about making a plan, scope, and framework. 

The fact that automation testers not only talk with developers for understanding code but also manual testers about test cases, and infrastructure engineers about integration to build up the final product. 

Solution: Establishing a collaborative environment, such as a specific point of contact in each process, clear expectation and the responsibility of members will help everyone to deliver the information fast and conveniently. Plus, active involvement and a transparent framework will develop your unique company culture.

 

5. Difficulties in lab management

A device lab that can match the scope of automation testing has to be a big one. As some of the teams prefer building and maintaining their own device labs, this can be quite extravagant.

For every operating system, there are different versions of browsers and different devices. To fully capture and exploit the utility of this device lab, the up-to-date feature and lab maintenance has to be assured, hence the high cost.

Besides the spiking cost of having your own lab, lab management also poses a great challenge In today’s competitive world, teams need to have the ability to conduct a test at any time.

Your solution needs to provide open access to the lab and equips teams with the right tools to run and perform tests. This ultimately helps you be adaptable and keep pace with the new releases.

 

Solution: Cloud-Based Test Lab

Having a cloud-based lab is key for continuous testing unless there are some special testing requirements/scenarios with IoT, special networking (especially in the Telco space), etc.

 

To sum up, automation testing supports payoffs effectively and is a great method for companies to speed up progress; however, test automation can not completely replace human intelligence. We still need humans to make the orientation in the whole process of automation testing to avoid or reduce the challenges in automation testing.

 

Want to find the solutions for the automation testing challenges? Contact LQA now for FREE consultation with our specialists and experts.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

Singapore, Vietnam, Malaysia and Indonesia are the centers for technology and software development in Southeast Asia. Therefore, software testing engineers are one of the most in-demand position. This report will be helpful for managers who want to figure out the differences of a tester’s salary in these countries.

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1. Software Testing Salary Range

Software testing salary range in Southeast Asia

[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_single_image image=”18310″ img_size=”full” alignment=”center”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]Among the four countries, Singapore has the highest payment range for software testers. On average, testers will receive $5100 per month. The maximum salary that one can be paid is $7980; meanwhile, the minimum is $2660. Malaysia stands in the second position in terms of payment. However, its maximum amount of payment is almost four-time less than Singapore’s. The minimum, average and maximum salary of Malaysian testers consecutively are $690, $1270, and $2030. Of the four representative countries, Vietnam has the lowest salary range. It only costs the manager $330 to $2000 per month to hire a software tester here. The average salary of a Vietnamese is $650, which is three-time less than Singaporean. Nevertheless, the maximum payment for the job is almost equal to Malaysia and higher than in Indonesia. In one month, Indonesian testers can get $360 for the lowest, $720 for the medium, and $1120 for the highest payment.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Software testing salary range in different regions

As seen in the chart above, remote team pricing is broken out into two tiers: Asia and everywhere else. In Asia, the average hourly rate is $24.62/hour, whereas the rest of the world commands higher prices averaged out around $38.67/hour.

A decade ago, there was a 400% difference in pricing from the lowest-priced region to the highest-priced region. Now the range has been cut in half. This ever-narrowing range of prices supports SourceSeek’s guiding principle that the global software market is an efficient one with enough demand to bring consistent pricing that is affected by a small set of characteristics such as location, language skill, proximity, etc.

Outliers are rare. As teams in Eastern Europe slowly set their rates higher and higher, there is enough demand to raise rates in less competitive regions accordingly and still remain competitive. The notable exception is India, where pricing trails the worldwide market due to the sheer volume of supply combined with ongoing reputation issues. There is increasing evidence that China is also beginning to see a similar trend, and will continue to have difficulty entering the global software market.

2. Software Testing Salary Based on Seniority

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Junior Software Tester Salary

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Junior software testers often have less than two years of experience. Within this level, Singaporean testers get paid the most with $3200 per month. It quadruples the salary of a Malaysian tester, who is paid $780. Ranking in third place, Vietnam has a monthly payment of $690, fewer than the second-place $88. The country having the lowest payment for a junior software tester is Indonesia, with $570 a month. It is five-time less than Singapore.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”15px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Senior Software Tester Salary

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If a tester is promoted to a higher level, their salary will be increased. The monthly salary of a QA engineer in Singapore will rise by $1700 to reach $4900. Meanwhile, the salary of a senior tester in Malaysia ranks second place at $1050 per month. Receiving $180 fewer is a Vietnamese tester with a payment of $870. Indonesian tester’s salary is the lowest, which takes employers $770 per month.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”15px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Software Testing Lead Salary

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To hire a software testing lead, an employer has to pay $6400 per month in Singapore. The figures in Vietnam, Malaysia, and Indonesia representatively are $990, $1460, and $1060. It is noticeable that Vietnam is the one who has the lowest salary, where the tester gets a sixth-time fewer than the highest payment.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”15px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Head of Software Testing Salary

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The salary of the Head of software testing in Singapore is significantly high compared with the other three countries. Tester at this level will be paid $7900 a month, four-time higher than a tester on the same level in Malaysia. Vietnamese and Indonesian testers monthly income are both in the range of $1300, but Indonesian man gets extra $60 which makes Vietnam the lowest pay country for this position.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

3. Salary Based on Education

[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_single_image image=”18322″ img_size=”full” alignment=”center”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]All four nations show a similar pattern in the chart, which is they will pay a higher salary for tester having higher education level. Besides, with the same degree, testers in Singapore get paid drastically higher than the rest. A tester who holds a certificate or diploma will gain $2660 a month in Singapore, which is eight-time higher than Vietnam and Indonesia, and a fourth-time higher than Malaysia. If the tester gets a bachelor’s degree, he or she will be paid $5100 in Singapore. This number is one-fifth in Malaysia ($1270), $720 in Indonesia, and $650 in Vietnam. Singaporean master’s degree owner will be paid $7980 a month, following by Malaysian and Vietnamese who get $2030 and $2000 representatively. The lowest-paid master’s degree holder is an Indonesian software tester, who gets $1120 per month.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]Although there are other countries in South East Asia, the four nations above are representative of the information technology center. Through the article, we showed general guidance of software tester’s salary in Singapore, Vietnam, Malaysia, and Indonesia. All the figures are collected from reliable sources, including Persol Kelly, Michael Page, and First Alliances. Hopefully, the article can be a reference when managers make their decision to hire a software tester. Nevertheless, if managers face difficulties in recruitment, there are still other alternatives such as purchasing software testing outsourcing services.

With a score of 82, Eastern European countries garnered the highest score of any region featured in this report and just edged out East Asia with a score of 80. Eastern Europe has an established reputation for having a mature and robust educational system, and many vendors in the region leverage that reputation to claim that the ‘best developers in the world’ come from Eastern Europe.

Eastern European educational excellence is focused primarily around math and science. The Organization for Economic Co-operation and Development (OECD), which measures 70 countries in reading, math, and science, found that Eastern European countries outperformed other countries featured in this report by an average of 11% in math and 10% in science.

So, while the much-touted claim of ‘best developers in the world’ may be a bit strong, Eastern Europe’s reputation for strong education is well supported by data. While a strong general education is certainly important for a successful IT education, a high score in the UN data doesn’t always result in top IT education, and vice versa.

4. Team composition

Average years of experience is a very informative metric when assessing the maturity of a region as a whole. It takes many years for developers to gain experience and move into management and leadership, making truly senior software engineers difficult to find.

This is exacerbated by brain drain in many countries since many of the most experienced engineers may move on to other more promising regions. Eastern Europe suffered from a bit of brain drain in years past, but for the most part there are adequate opportunities available for software professionals and no need to leave to find work. The presence of so many seasoned professionals also feeds the IT ecosystem, which we’ll look into later in the report.

Lotus Quality Assurance is the first independent software testing company in Vietnam. As a Silver Partner of ISTQB, we provide you a talented team of testing with international experiences. Contact us to be aided with your software testing project.[/vc_column_text][/vc_column][/vc_row]