Until the world arrived in the 21st century, a successful business was attributed to one or more of the following things: making a great product, mass production, excessive marketing, efficient distribution- making sure your product had a pervasive market presence, good customer service, etc. The end of the last century and the last 19 years of this century have brought radical changes in almost all aspects of the world. Digital Transformation on a massive scale has sent the likes of Kodak packing, and new giants like Amazon, Google have emerged.

The key factor that distinguishes a great business from an average one isn’t marketing or cheap production, but User Experience i.e. UX. New generation customers have become well aware of their needs and various alternatives in the market. Companies have started paying serious attention to this metric, which is why UX has started to gain significance.

What is UX Testing?

UX Testing, also called Usability Testing is a process, where target end-users of a product use it and evaluate it based on preset parameters of user experience. The most important among these are:

• Ease of Use
• Whether the product functions as originally intended
• Its adaptability in managing controls.

Elements of UX Testing

1. Planning

At this stage, we decide what we exactly want from the product. This helps us identify the exact parameters of the test. We decide what constitutes a successful test. Make sure that you know what possible outcomes of the test area and what they imply. Other aspects are the Usability Testing technique, the format of the test report, test demographics, etc.

2. Focus Groups

Focus groups are widely used in UX testing. A focus group is a group of people (say 5-10) whose job is to sit together and discuss a product in detail, its pros, and cons, and report them to the company. A moderator conducts the session and guides them to the relevant areas of study. Focus groups are important because they represent the target user. They give us a firsthand experience of the potential user and let us grasp the true magnitude of the product’s effect on them. The important thing to remember is that one focus group is not representative enough, so at least a few of them should be used. In the mobile app market, focus groups can be, and should be used to find out what an average user wants from the app, and how well does the app satisfy them.

3. Tree Testing

Tree Testing is a wonderful technique that guides you and the UX designer to see how well users collaborate and discover products or components in the website model. Because of it, we can see what went wrong and how to fix it. It also tells us how close/far are we from the product in our vision. The methodology determines whether your data design structure is understood by the user or not.

4. Prototype testing

This type of testing can be utilized for a product on a pre-development basis. Its entire working journey is captured and tested in this phase. This is known as the primary testing phase. A UX designer makes the prototype and structure work processes. Design prototype testing helps us get a clear picture of the product’s usability. This helps us in the next phase, i.e. product development. The following are some points to be observed:

• Characterize the objectives and budget for the testing process
• Work hard on the beginning phase tests to uncover particular zones that may require upgrades
• Choose the appropriate prototyping tool
• Use a measuring device for the prototype to accumulate analytics from the users

Moderated Usability Testing

Moderated usability testing is used to get comments on the product from actual customers. In such a test, moderators- the test conductors interact with test participants. They are in continuous contact with them, guide them about the product, and note their responses, queries. This helps us understand what the customers want. Live interaction with the test participants ensures real-time observation, query-solving, and it can’t be effectively substituted by other methods. This type of testing is used when the product is at the stage of designing.

Moderated Usability Test is run to find out the potential flaws of the concerned product. The data acquired by conducting this test is used to evaluate the level of competence and readiness of the product. It enables us to manage the risk of investing huge amounts of resources in the project.

Un-moderated Usability Testing

This technique is used when we need a large sample for testing. The test is usually led through a platform or a website that records the session, tracks the metrics, and randomizes groups and tasks. A portion of the accessible tools can get you results in a few hours so that you can keep the development procedure continuous.

Maintaining live records

Maintaining live records, by taking notes at regular intervals, is necessary to learn from the test. It may help us in the future.

It helps to note as much as we can: what the participant is doing, what they say, where they go, what they do etc. One more important thing to remember is to record timestamps and quotes in the case of important events. We should also look for verbal signs and facial prompts if we’re physically present. The note-taker may be tempted to classify problematic and non-problematic areas right there during the test. However, this might make them prone to biases, so it should be avoided.

Tools available for UX Testing

UX testing or Usability Testing is a continuously improving area. Which means its scope is also constantly evolving. With ever more complex apps coming to the market everyday, it’s become necessary to have some usability testing tools at our disposal. Here are 5 of the best tools that are in the market for UX Testing.

1. Microsoft Inclusive Design

This is a software focusing on inclusive design, as the name suggests. Their plus point is that designing for individuals with inabilities results in structures that are useful for people across the globe. The software has comprehensive design standards to follow such as learning from diversity, activity cards describing case studies and tools, videos showing the inclusive design in action, among others.

2. IDEO Design Kit

This is another amazing software from IDEO. It contains ‘Mindsets,’ which have models for design strategies, sketch main design standards, and case studies indicating how ‘human-centered design’ has driven actual outcomes. IDEO focuses more on human-centered design than on user-centered design.

3. Design Practice Methods

The Design Practice Methods focuses on Human-centered design techniques, alongside more great design techniques, for example, material testing and mood boards. Methods can be searched by category such as Creative and Analytical and with a little classification and models presented for every strategy.

4. Crazy Egg

It has a Heatmap that helps the website find out where every user has tapped on the site. The Confetti provides insights concerning search terms and visitor sources. And, the Overlay that will analyze the number of clicks per page component. The Scrollmap – demonstrates how far down on the page a guest has frequently looked over.

5. Usabilla

This tool has a wide range of features to be employed by UX testers, making it a very exhaustive ordeal bundle. A couple of the elements that can be actuated are: mobile feedback, exit reviews, click heatmaps, directed feedback forms, and feedback widgets that assemble information through emails. Any user can give it a shot on a 14-day free trial mode otherwise they have a month to month, a yearly pricing structure.

Conclusion

In a world where user experience has become THE buzzword, UX Testing is very important to ensure that your app or site or product fulfills the customers’ needs. Before spending huge amounts of money, getting the prototype tested is a smart thing to do. As said earlier, this a constantly evolving field, making testing very complicated. Which is why getting help from experts becomes very important.

How VTEST can help:

VTEST has ample experience in the field of UX Testing. We understand the important role played by user experience in the making of a successful software development company. Whatever your product is, we’ll help you achieve your goal.

VTEST it!

 

For any software to work properly, its code needs to be accurate. The software improves with time, and therefore its code undergoes continuous change. However, even a slight change in the script may alter its functioning considerably. Our goal is for the software to work exactly as intended. This is why it must be tested regularly, especially after every change is made to the script. Manually testing the code every such time is extremely tedious. This is why functional automated testing is useful. But let’s first see what Functional Testing means.

What is Functional Testing?

Functional testing checks whether every function of your code works as originally intended. It’s about testing the functional aspects of any given software. The testing process is basic and similar to the general testing manifesto.

You send appropriate inputs and compare the actual outputs to the expected outputs and then figure out how to remove the variance if there’s any. Since this is the primary process to ensure that the software is of the required quality, you may have to repeat it. It saves time to automate the process.

Functional Automated Testing

Functional Automated testing facilitates the automatic execution of your functional test cases. Manual testing, on the other hand, requires you to execute the test case step by step, which is lengthy. Automating functional test case saves both time and effort. It’s done with zero human intervention, thereby minimizing human error.

Drawbacks of Manual Testing

We know that it is important to test any software manually as it has several benefits but to be honest, Automations save a lot of time and effort in the process of testing. Though it is the original and old school method of testing, Manual testing has its flaws. From human errors to time consumption, the list is significantly intense.

• Extremely Lengthy
• Repetitive
• Possibility of Human Errors
• High resource consumption

Now that we have seen the setbacks faced while manually testing any given software, we will try to understand why is it important to execute functional testing in an automated approach.

Why Functional Automation Testing?

Nowadays, the software is developed very quickly. Alterations and enhancements are done continuously, and equally rapidly. Testers must match the pace by performing tests smartly. And that is why we need functional automation testing.

Companies are now following a practice called DevOps, which integrates Software Development and IT Operations to reduce the SDLC and deliver great quality software. These frequent integrations and improvements in the code require test cases to run quickly and accurately. And automation functional testing assures exactly that.

Apart from saving time and resources, Functional automated testing provides:

• Accurate Benchmarking
• Minimum Human Error
• Broad Test Coverage
• Enhanced Reusability
• Faster Finishing ( And Release)
• Quicker Bug Reports

These were some of the benefits automation provides. Now we will take a look at how functional automation testing can be seen as a tool for software development.

Functional test automation as a software development tool

Automating a test case involves applying a code to test a particular code. Developing this code is complicated just like developing any other software and therefore has the same challenges. Following the best practices in software development is the key to developing a flawless testing code. And a flawless testing code is the key to successfully perform Functional Automated Testing.

Should you automate all your test cases?

Automating all the test cases may not be a good idea. We should take into account the following things to determine which test cases to automate:

• Deterministic test cases
• Lengthy test cases
• Unit test cases
• Stress/load test cases
• Test cases are required to run on various browsers, systems, etc.
• Test cases that don’t require manual communication
• Test cases involved with fiscal-related areas of the software
• Test cases involved with risk areas of the software
• Tests that require running on different data sets
• Test cases difficult for manual testing

Despite this, the criteria for automated testing may vary from case to case.

We now head to the practical aspect of Functional Automated Testing i.e. ROI. Return on Investment.

RoI: The business factor

ROI or Return on Investment is the deciding factor in any business decision. Whether to automate your test cases or not, is also largely dependent on ROI. Investment decisions are generally made using estimates and past figures because we can’t exactly predict what the conditions in the future will be. A well-known formula for estimating the cost of automation is given below:

Automation Cost = automation tool cost + cost of the labor to automate test cases + maintenance cost.

This cost should be compared with the manual testing cost before making the final decision. It should be kept in mind that manual testing cost is a variable cost (owing to human labor). That is why it keeps piling up as test cases increase. Automation cost, on the other hand, is fixed and therefore is reduced (per test) as the test cases increase. Other factors may vary from case to case.

What Not to Automate

Even good things shouldn’t be overdone. By executing all the test cases by automation, one might allow errors to occur in some test cases. Remember, extremities won’t take you anywhere. All test cases are not designed to be automated. There’s a limit to what test cases you can automate. Here are some that you shouldn’t:

• Single performed test cases
• Usability test cases
• Customized test cases
• Test cases that do not have a predictive result

Discretion is necessary to decide which tests to automate. Functional test case automation can be highly beneficial for you if done wisely.

Conclusion:

To summarize,

We discussed what functional testing is and its benefits when done in an automated way. Along with that, we looked at some much-discussed drawbacks of manual testing. Coming on next to the need for automation in functional testing. ROI i.e. Return on Investment also cannot be ignored.

On an ironic note, we looked at some of the flaws of over-executing automation. Because as we all know, even good things shouldn’t be overdone.

We all know that among all the types of testing, functional testing is one of the most important phases of the Software Testing Life Cycle i.e. STLC. Most of the time, it is done manually, with testers rigorously working day and night to make sure the functions of the software are running smoothly. This makes the whole process very tedious. It takes a lot of time, effort, manpower, and money to execute functional testing manually. To make it more efficient, automating function test cases is the right thing to do. It saves both time and money and testers can give proper attention to the parts of the testing process which require more focus.

How can VTEST help?

VTEST understands that time is money. We’ll advise you on what tests you need to automate, how best to do it. Once we take over, you’ll realize your time is spent to focus on your core areas, and not worrying about how long it’s going to take to release your new product.

VTEST it!

 

Related: Best Practices for Test Automation Framework

Artificial intelligence has moved far beyond the “bots” era. In 2020, the conversation was about simple test bots automating repetitive scripts. By 2026, AI is not just running tests — it is designing them, reasoning about failures, and autonomously correcting the application under test. This post examines where AI and software testing have arrived, and where the next wave is taking the industry.

From Bots to Intelligent Agents: A Fundamental Shift

The early use of AI in software testing focused on record-and-playback bots and basic ML models that flagged flaky tests. These tools reduced manual effort but still required constant human oversight to maintain scripts, interpret results, and decide next steps.

The arrival of large language models (LLMs) in 2022–2023 changed the foundation. Models like GPT-4 and Claude could read code, understand intent, and generate test cases from plain-English descriptions. By 2024–2025, these models were embedded into development pipelines as autonomous testing agents capable of multi-step reasoning — not just running a script, but deciding what to test, how to test it, and what to do when something fails.

What Is Agentic Testing?

Agentic testing is the current frontier of AI in QA. An agentic testing system uses one or more AI agents that can:

• Analyse source code, user stories, or API contracts to generate relevant test cases
• Execute tests across browsers, devices, and environments without human instruction
• Interpret test failures and attempt automated root-cause analysis
• Self-heal brittle selectors and update test scripts when the UI changes
• Report findings in structured formats for developers to act on immediately

Tools like Playwright’s AI mode, Applitools, Mabl, and purpose-built agentic QA platforms (Octomind, QodexAI) now offer varying degrees of this capability. Enterprise teams are moving from “automated testing” to “autonomous testing” — where the QA pipeline runs with minimal human touchpoints.

Key AI Testing Capabilities in 2026

1. LLM-Driven Test Generation

QA engineers now prompt LLMs with acceptance criteria, user stories, or API specifications to generate comprehensive test suites. This dramatically reduces the time from requirement to test coverage. Tools like GitHub Copilot, Cursor, and dedicated QA AI assistants produce Selenium, Playwright, Cypress, and REST Assured code on demand, which human testers then review and refine.

2. Visual AI Testing

Computer vision models compare UI screenshots pixel-by-pixel across thousands of device/browser combinations in seconds. Tools like Applitools Eyes use AI to distinguish meaningful visual regressions from acceptable rendering differences, eliminating the false-positive noise that plagued traditional screenshot comparison tools.

3. Predictive Test Selection

ML models analyse commit history, code change patterns, and historical defect data to predict which test suites are most likely to catch failures for a given code change. Instead of running all 10,000 tests on every pull request, teams run the 500 tests most relevant to the changed modules — reducing CI pipeline time by 60–80% without compromising coverage.

4. Self-Healing Automation

One of the biggest costs in test automation is maintaining scripts when the UI changes. AI-powered self-healing tools (Healenium, Testim, Mabl) detect broken locators at runtime and automatically identify the correct element using contextual signals — element type, position, surrounding text, ARIA attributes. This keeps automation suites green without constant manual maintenance.

5. AI-Powered Performance and Security Testing

AI is now being applied beyond functional testing. In performance testing, AI agents dynamically adjust load patterns based on real-time system metrics, identifying breaking points more efficiently than static load scripts. In security testing, AI tools fuzz APIs and UIs with adversarial inputs far beyond what manual penetration testers or rule-based scanners would attempt.

The Changing Role of the Human QA Engineer

The most important question the industry has debated since 2023 is: does AI replace QA engineers? The evidence through 2026 points to transformation, not replacement.

What AI does well — exhaustive regression coverage, consistency, speed, 24/7 availability — it does far better than humans. What humans do well — understanding product intent, identifying edge cases that “technically pass but feel wrong”, communicating quality risk to stakeholders, ethical judgment — remains irreplaceable.

The QA engineers thriving in 2026 are those who have repositioned as AI orchestrators: they design the prompts, validate the AI-generated tests, set the quality bar, and focus their manual effort on exploratory and experience testing where human intuition adds the most value. Skills in prompt engineering, Python scripting to direct AI agents, and interpreting LLM output critically are now core QA competencies.

Risks and Challenges of AI-Driven Testing

AI in testing is not without pitfalls. Teams adopting AI-driven QA tools need to manage several real risks:

• Hallucinated test cases: LLMs can generate plausible-looking but logically incorrect tests that appear to pass while validating nothing meaningful. Human review of AI-generated tests is non-negotiable.
• Overconfidence in AI coverage: High AI-generated test counts can create a false sense of security. Coverage metrics need to be evaluated for depth and relevance, not just quantity.
• Data privacy in AI tools: Sending production code and test data to external LLM APIs raises confidentiality concerns. Teams working on sensitive applications should use self-hosted models or carefully vet the data handling policies of third-party tools.
• Tool fragmentation: The AI testing tool market is fragmented and fast-moving. Betting on a single vendor or framework requires careful evaluation of maturity and long-term viability.

Where AI Testing Is Headed

Looking at the trajectory, three developments are set to define AI in software testing over the next two to three years:

• End-to-end autonomous QA pipelines: Systems where AI handles the entire testing lifecycle — from requirement analysis to test execution to sign-off reporting — with humans reviewing outputs rather than directing every step.
• AI co-pilots in IDEs: Real-time quality feedback as developers write code, not just at commit or CI time. AI agents that catch testability issues, suggest test cases, and flag security anti-patterns inline.
• Multi-agent QA systems: Collaborating AI agents with specialised roles — one agent for functional tests, another for performance, another for security — coordinating across a shared quality model to deliver comprehensive coverage simultaneously.

How VTEST Approaches AI-Driven QA

At VTEST, we have integrated AI-assisted testing into our service delivery across multiple client engagements. Our approach is pragmatic: we apply AI tools where they create measurable ROI (regression automation, visual testing, test generation speed) and keep human expertise at the centre of quality strategy, exploratory testing, and stakeholder communication.

We work with clients to evaluate which AI testing tools fit their tech stack and risk profile, build the internal capability to use those tools effectively, and establish governance frameworks that keep AI-generated tests honest. Whether you are just starting to explore AI in your QA practice or looking to build a fully autonomous pipeline, our team can help you move at the right pace.

Related: Agentic Testing: The Complete Guide to AI-Powered Software Testing

All the youthful activities in today’s world are driven by technology. May it be dating culture, newer innovative business startups, delivery applications… The list can go on. If we look closely, we will observe that the face of all these forces of today’s digital world is software. Though it can be seen and used in various formats like websites, applications, etc., it is the single most impactful and game-changing piece of technology in the current era.

A software, like any other product, is developed to meet a predetermined goal. The purpose of software testing, in simple terms, is to find out whether the software meets that particular goal, to assess its strengths and weaknesses, areas of improvement.

Software testing is an independent activity that determines whether your software is ready to be launched in the target market. Some of the common characteristics tested include its design, development, response to all kinds of inputs, execution of the desired functions within the right time-frame, and overall efficiency.

Software testing can be performed at different stages of the Software Development Life Cycle (SDLC), as per the choice of the developer. However, it’s advisable to perform it as early as possible, as it significantly saves resources. If not, you could do it as soon as the most basic prototype is developed.

The software testing process usually includes these steps:

1. Planning – Plan the whole process systematically.
2. Analysis and Design – Analyze and design proper test cases to execute while testing.
3. Implementation and execution – Implement and Execute the test cases to find out the bugs/errors.
4. Evaluation and reporting – Evaluate the whole testing result mainly comprising of all the bugs found and draft a report based on the evaluation.
5. Test closure – Finish the process by sending the report to the developers’ team.

One should note that this process should be repeated certain times to finalize the testing process before the release of the application or software.

Every company is keen to get its software tested in a thorough, comprehensive manner to find out and fix all the glitches. However, it’s not easy to know how far you’ve come towards your goal. There’s always scope for improvement. Here are some general guidelines:

 

• Define a process

 

Before going ahead with actual testing, it certainly helps to define a clear-cut, expert-approved testingprocess. Instead of being strictly followed, this can act as a guideline. We can make this process as a baseline and tweak it as we go on developing it. But a process is only as good as it’s executed, so it should be designed with a practical approach.

 

• Involve testers in the development

 

If testers are involved from the beginning throughout the process, they can get a good, close look at the product, which helps them devise a comprehensive testing process.It also helps find out and fix glitches early on, saving resources. The more they know and understand, the more they can contribute, it’s that simple.

 

• Maintain records

 

In your day-to-day life, you store all the important documents you need in your cupboard. You store food in the fridge. Why do you do it? It’s because storing these things helps you do your activities in the future more smoothly. Just like this, it’s highly useful to store all the relevant information related to a product, from its testing plan to its product details, in the form of documents, reports in a separate folder, as it becomes easier for us to access it. This way, we don’t have any last-minute regrets, and helps us streamline our testing operation.

 

• Have Prototypes ready in advance

 

It’s safe to have prototypes ready in advance for the fourth stage. This helps improve the productivity of the developer as well as the tester. It saves time. It can also help the testers find out if the requirements mentioned for the product can be tested. It’s pertinent, however, that these prototypes; test cases are easily understood by the tester.

 

• Be Critical

 

The job of a tester, any tester, is to independently evaluate the product they’re testing. It’s important to be unbiased before, while, and after testing. One can’t assume that software is perfect regardless of how good the developer is. Start by thinking that there are glitches, and then rule them out step by step. If you’re looking for glitches, you’ll find glitches, which helps us fix them. That should be the aim.

 

• Divide the product into parts

 

The testing team can divide the entire product module into smaller parts. This ensures that even the smallest aspect of a particular application doesn’t remain unnoticed. The division of the entire logic into smaller modules makes it simple to identify. Any glitches that come up are easy to spot and fix. Additionally, doing this saves time, contrary to what we might think.

 

• A Comprehensive Testing Report

 

A clear and detailed bug report is a must if you want to deliver a flawless product. Apart from leading the developers to the bugs and helping them fix them, it saves valuable time. The report should be clear and understandable to the developers and the testers themselves, to work together to deliver the product.Check out our blog on how to draft a good bug report to get a clearer picture.

 

• Create a good test environment

 

The testing team must test a product in a simulated environment the same as the one it’s intended to be used in after it’s launched. This is to ensure that there are no glitches or bugs that are missed out during the product’s testing.

Ex. If a developer has added certain configurations or run scripts but has missed adding the same in release document or configuration file. The testers may not be able to thoroughly test the product, resulting in some glitches in the end.

 

• Developer-tester coordination

 

This is the most important, yet the most difficult point to realize. Any great product is a combined effort of the testing team and the developing team. Coordination between these two teams (Ex. discussing a testing report, brainstorming ways to fix the glitches) pays off. It is equally important to keep an e-trail of all such discussions so that you can refer back to them, just in the case.

Conclusion

As discussed earlier, Software Development is not a flick that can be ignored. It is here to change the game. If we need to go ahead into a brighter and smarter future, Software Development is the path on which we should walk.

That’s why software testing is a must. It is like repairing the road to the future. If we do not remove these bumps called bugs on this road of coding, we as humans might fail in the long run.

Software testing is a necessary activity before its launch. What we need is a well-thought-out plan, a critical approach, and a nice working relationship between the testing team and the developing team, who are committed to delivering a flawless product. Simple enough to understand, it’s not so easy to realize.

How VTEST can help?

We at VTEST strive to excel. With our tea of intelligent, young, and dynamic testers, we are changing the game of software testing. We understand how important it is to streamline a testing process, to stick to it without losing the main purpose of testing. Our experts can work well with developing teams to deliver an error-free product.

VTEST it!

 

Mobile applications are now the primary interface between businesses and their customers. With over 7 billion smartphones in use globally and users spending more than 4 hours per day on mobile apps, the quality of a mobile application directly determines business outcomes. A single crash, a slow checkout flow, or a broken permission request can translate to immediate uninstalls, negative reviews, and lost revenue. This guide covers everything QA teams and product organisations need to know about mobile app testing in 2026.

Why Mobile App Testing Is More Demanding Than Ever

Mobile testing has grown significantly more complex over the past four years. The reasons are structural:

• Platform fragmentation: Android runs across thousands of device models from dozens of manufacturers, each with custom OS modifications. iOS is more controlled but introduces significant testing scope with each annual major release.
• New device categories: Foldable phones (Samsung Galaxy Z Fold series, Google Pixel Fold) and large-screen Android tablets require applications to support dynamic layout changes and multi-window modes that conventional phone testing does not exercise.
• New OS versions: iOS 18 and Android 15 (released in 2024) introduced changes to privacy permissions, background app processing limits, predictive back gestures, and API behaviours that broke existing apps and required explicit test coverage.
• AI features in apps: On-device AI (Apple Intelligence, Google Gemini integration) introduces non-deterministic behaviour that conventional test automation cannot assert against with simple string matching.
• 5G and connectivity variability: 5G availability is uneven globally. Apps must be tested under varying network conditions — from gigabit 5G to congested 4G to offline mode.

Types of Mobile App Testing

Functional Testing

Verifies that every feature of the application works as specified. This includes all user flows, form validations, navigation, error states, and edge cases. Functional testing covers both happy paths (expected user behaviour) and negative paths (invalid inputs, network errors, permission denials). For mobile, functional testing must account for OS-level interruptions: incoming calls, notifications, low battery warnings, and app switching.

UI and Usability Testing

Validates that the user interface renders correctly and that interactions feel natural on a touchscreen. This includes checking tap target sizes (minimum 44x44pt recommended by Apple, 48x48dp by Google), gesture handling (swipe, pinch-to-zoom, long press), screen orientation handling, and accessibility compliance (VoiceOver on iOS, TalkBack on Android). UI testing across different screen sizes and resolutions must be part of every release cycle.

Compatibility Testing

Ensures the application works correctly across the target device matrix. Given the enormous range of Android devices and the fragmentation of OS versions in active use, compatibility testing requires a strategic approach to device selection. Industry guidance suggests covering the OS versions that account for at least 90% of your user base. Cloud device farms (BrowserStack App Live, AWS Device Farm, LambdaTest Real Device Cloud) make this practical without maintaining a large physical device inventory.

Performance Testing

Measures how the application behaves under load and in constrained conditions: launch time, screen transition speed, memory usage, battery consumption, and behaviour under slow or intermittent network connections. Tools like Android Profiler, Xcode Instruments, and Firebase Performance Monitoring provide the telemetry needed for performance analysis. Specific targets vary by category — a banking app is expected to launch in under 2 seconds; a game may have different acceptable thresholds.

Security Testing

Mobile applications present distinct security risks: insecure data storage, inadequate transport layer security, improper session management, and platform permission over-provisioning. The OWASP Mobile Security Testing Guide (MSTG) and the Mobile Application Security Verification Standard (MASVS) are the definitive frameworks for mobile security testing. Every app handling user credentials, financial data, or personal health information must be tested against these standards before release and after significant updates.

Interrupt Testing

Tests how the application handles real-world interruptions during use: incoming calls, SMS messages, push notifications from other apps, system alerts (low battery, storage full), network drops, and device rotation. Interrupt testing frequently uncovers crashes and data loss bugs that functional testing under ideal conditions misses entirely.

Localisation and Internationalisation Testing

For applications serving global markets, localisation testing validates that translated text fits UI layouts (German and Arabic translations frequently overflow containers designed for English), date/time formats are correct, currency symbols display properly, and right-to-left language support (Arabic, Hebrew) works correctly throughout the application.

Mobile Test Automation in 2026

Manual testing alone cannot scale to cover the device matrix and regression scope required for modern mobile releases. Automation is essential, but mobile automation has historically been challenging — slow, brittle, and difficult to maintain. The tooling has improved significantly.

Appium

The dominant open-source cross-platform mobile automation framework. Appium 2.x introduced a plugin architecture that significantly improved flexibility and driver management. It supports iOS (via XCUITest driver) and Android (via UIAutomator2 driver) using a WebDriver-compatible API, making skills transferable from web automation. The main limitation is speed — Appium tests run slower than native instrumentation tests.

XCUITest (iOS Native)

Apple’s native UI testing framework for iOS. XCUITest tests run directly in the Xcode simulator and on physical devices, are maintained as part of the Xcode project, and run significantly faster than equivalent Appium tests. For teams with iOS-focused products, XCUITest is the preferred automation layer for regression coverage.

Espresso (Android Native)

Google’s native Android UI testing framework. Espresso tests are tightly integrated with the Android build system, run fast, and have first-class support in Android Studio. Like XCUITest, Espresso is the preferred choice for Android-specific automation over Appium where team capacity allows.

AI-Powered Mobile Testing Tools

A new generation of tools is addressing the maintenance and scaling challenges of mobile automation. Platforms like Waldo, Sofy, and Repeato use AI to generate and maintain test scripts from recordings of manual test sessions, automatically adapting to UI changes. These tools are particularly valuable for teams that need broad test coverage but lack the engineering resources to build and maintain large native automation suites.

iOS 18 and Android 15: What QA Teams Need to Know

iOS 18 Key Testing Impacts

• Apple Intelligence: AI writing tools, photo clean-up, and summarisation features require testing of AI-powered UX elements. Apps that integrate Writing Tools or Siri enhancements must validate these integrations specifically.
• Home Screen customisation: Users can now place app icons anywhere and tint them. Test that app icons remain recognisable under various tint configurations.
• Control Centre customisation: Third-party controls added to Control Centre must function correctly and not conflict with app states.
• Privacy permission updates: New granular controls for contacts access require apps to gracefully handle partial permission grants.

Android 15 Key Testing Impacts

• Predictive Back Gesture: Android 15 makes the predictive back animation a requirement for apps targeting API level 35+. Back navigation must be explicitly handled and tested.
• Edge-to-edge display enforcement: Apps must render content edge-to-edge and handle system bar insets correctly. Layouts not updated for this requirement will have UI overlap issues.
• Health Connect integration: Apps using health data must test updated Health Connect APIs and the new permission model.
• Large screen and foldable support: Google has strengthened quality guidelines for large-screen compatibility, making foldable and tablet testing more important for Play Store ranking.

Building a Mobile Testing Strategy

An effective mobile testing strategy balances coverage, speed, and resource efficiency. The key decisions:

• Device matrix: Define the minimum viable device set based on your analytics. Cover the top OS versions by user base (typically the latest two major versions per platform), top device models by market share, and any device categories relevant to your audience (tablets, foldables).
• Automation scope: Automate regression coverage for all critical user journeys. Reserve manual testing effort for exploratory testing, new feature validation, and device-specific edge cases.
• Cloud vs physical devices: Cloud device farms for broad compatibility coverage; physical devices for performance profiling and hardware-specific features (camera, NFC, biometrics).
• Shift-left: Run fast unit and integration tests in CI on every commit. Run UI automation on every pull request targeting critical paths. Run full device matrix compatibility testing before release.

How VTEST Approaches Mobile App Testing

Mobile testing is one of VTEST’s core specialisations. Our team combines manual exploratory testing expertise with automation capability across Appium, XCUITest, and Espresso, running against real device clouds for compatibility coverage. We test across the full quality spectrum — functional, performance, security, accessibility, and interrupt testing — and adapt our testing scope to match your release cadence. Whether you need a one-time pre-launch audit or a dedicated mobile testing engagement integrated into your sprint cycle, our team can deliver the coverage your users expect.

Further Reading

• Mobile Software Testing for App Developer – A Beginners Guide
• Mobile App Testing: Android vs iOS
• Mobile Application Testing: Stepwise Method
• Mobile Game Testing: Usability and Functionality Testing
• Secure your Mobile App – 8 important steps

Related Guides

• Penetration Testing: Definition, Need, Types, and Process
• Best Practices for Test Automation Framework

Blockchain testing has matured considerably since the early days of simple cryptocurrency transaction validation. The rise of DeFi (decentralised finance), NFT platforms, Web3 applications, and enterprise blockchain implementations has created a complex, high-stakes testing discipline where errors are often irreversible and the financial consequences of bugs can run to millions of dollars. This post covers the current state of blockchain application testing in 2026 — what has changed, what remains uniquely challenging, and the five areas every blockchain testing programme must address.

The Blockchain Testing Landscape in 2026

The blockchain ecosystem has differentiated significantly since 2020. The major segments requiring distinct testing approaches:

• DeFi protocols: Decentralised exchanges, lending platforms, yield farming, and staking protocols built primarily on Ethereum, Solana, and Layer 2 networks (Arbitrum, Optimism, Base)
• Web3 applications: Browser-based dApps (decentralised applications) with wallet integration (MetaMask, WalletConnect, Coinbase Wallet) and on-chain interactions
• NFT platforms: Minting, trading, and royalty distribution for non-fungible tokens — including the ERC-721 and ERC-1155 standards and their cross-chain variants
• Enterprise blockchain: Permissioned blockchain implementations using Hyperledger Fabric, Corda, and Quorum for supply chain, trade finance, and identity management use cases
• Cross-chain applications: Bridges and interoperability protocols enabling asset and data movement between different blockchains — one of the highest-risk areas in the ecosystem

1. Smart Contract Testing

Smart contracts are the most critical testing target in the blockchain space. These self-executing programs run on the blockchain, handle real financial value, and once deployed, cannot be patched in the traditional sense — upgrading a contract requires a migration process or proxy pattern that itself introduces risk. The cost of smart contract bugs ranges from inconvenient (failed transactions) to catastrophic (complete fund loss).

Unit and Integration Testing

Smart contract unit testing uses frameworks like Hardhat (JavaScript/TypeScript), Foundry (Solidity-native, fast), or Truffle to test individual contract functions in isolation. Each function should be tested for: correct output under valid inputs, reversion under invalid inputs, correct event emission, access control enforcement (only authorised callers can invoke privileged functions), and boundary conditions (maximum values, zero amounts, empty arrays).

Integration testing verifies that contracts interact correctly when composed — a common source of bugs in DeFi protocols where multiple contracts interact in complex sequences.

Fuzzing and Property-Based Testing

Foundry’s built-in fuzzer and tools like Echidna automatically generate thousands of random inputs to find edge cases that manually written test cases miss. Property-based testing defines invariants — conditions that should always hold true regardless of inputs — and verifies them across a vast input space. For DeFi protocols, invariants like “total deposits always equals total withdrawals plus current balance” catch accounting errors that are invisible to unit tests.

Common Smart Contract Vulnerabilities to Test

• Reentrancy: An external call to an untrusted contract that allows it to call back into the original contract before the first execution is complete. The original DAO hack (2016) and many subsequent exploits used this pattern. Test that state changes occur before external calls.
• Integer overflow/underflow: Solidity 0.8+ includes built-in overflow protection, but contracts using older versions or unchecked arithmetic blocks remain vulnerable.
• Oracle manipulation: DeFi contracts that rely on on-chain price oracles (Uniswap TWAP, Chainlink) can be manipulated through flash loan attacks. Test that oracle price feeds cannot be manipulated within a single transaction.
• Access control failures: Functions that should be restricted to the contract owner or specific roles are accidentally left public. Test all privileged functions for correct access control enforcement.
• Flash loan attack vectors: Test DeFi protocols under scenarios where an attacker can borrow unlimited capital within a single transaction to manipulate prices or drain liquidity.

2. Security Auditing

For any smart contract handling significant value, a formal security audit by an independent security firm is essential before mainnet deployment. Security auditors (Trail of Bits, OpenZeppelin, Consensys Diligence, Sherlock) conduct manual code review, automated analysis using Slither and MythX, and economic attack modelling specific to the protocol design. Audit findings are categorised by severity and must be addressed before deployment.

Audits are not a one-time activity. Any significant contract upgrade, new feature, or integration with a new protocol should trigger a scoped re-audit of the changed code.

3. Web3 Frontend and Wallet Integration Testing

The frontend of a Web3 application presents testing challenges distinct from traditional web testing:

• Wallet connection: Test connection, disconnection, and reconnection flows across major wallets (MetaMask, WalletConnect, Coinbase Wallet). Test behaviour when the user switches networks, switches accounts, or revokes the application’s permission.
• Transaction signing flows: Test that transaction parameters displayed in the wallet confirmation prompt match what the application is actually submitting. Test user rejection of transactions (the application must handle rejection gracefully without crashing).
• Network compatibility: Applications supporting multiple chains (Ethereum mainnet, Polygon, Arbitrum, Base) must be tested on each supported network — contract addresses, gas estimation, and RPC behaviour vary by chain.
• Error handling: Test all blockchain error states: insufficient gas, transaction reverted, network congestion, RPC endpoint failure. Users must receive clear, actionable error messages rather than raw hex error codes.
• Gas estimation accuracy: Verify that gas estimates provided to users are accurate and that the application handles gas price spikes gracefully.

4. Performance and Network Conditions Testing

Blockchain applications have performance characteristics unlike traditional web applications. Transactions have confirmation times measured in seconds to minutes depending on the network and congestion level. Testing must account for:

• Transaction confirmation waiting: The application must correctly display pending states during the confirmation period and handle the eventual confirmation or failure
• Network congestion scenarios: Test behaviour when gas prices spike and transactions are stuck in the mempool for extended periods
• Layer 2 and bridge delays: Cross-chain bridges have multi-hour finality windows. Applications must manage long-duration pending states correctly
• Testnet vs mainnet differences: Testnets (Sepolia, Holesky for Ethereum) behave differently from mainnet in terms of gas costs, finality, and available liquidity. Testnet testing is necessary but not sufficient — pre-mainnet staging on a fork of mainnet provides more realistic conditions

5. Cross-Chain and Interoperability Testing

Cross-chain applications — bridges, multichain protocols, cross-chain messaging systems — are the highest-risk category in blockchain development. The majority of the largest DeFi exploits in 2022–2024 targeted bridge contracts, resulting in billions of dollars in losses. Cross-chain testing must address:

• Message passing correctness — that the payload sent on the source chain matches what is executed on the destination chain
• Replay attack prevention — that messages cannot be replayed on the destination chain after successful execution
• Failure handling — that failed messages on the destination chain can be identified and handled, and that the source chain is notified correctly
• Validator/relayer failure scenarios — testing behaviour when bridge validators or relayers go offline mid-transfer

Testing Tools for Blockchain Applications

• Hardhat: JavaScript/TypeScript development environment with built-in local blockchain, testing utilities, and mainnet forking capability for realistic DeFi testing
• Foundry: Solidity-native testing framework with the fastest test execution, built-in fuzzer, and excellent debugging tools. The current preference for security-conscious teams
• Slither: Static analysis tool for Solidity — detects common vulnerability patterns automatically
• MythX / Mythril: Symbolic execution-based security analysis for smart contracts
• Tenderly: Transaction simulation, monitoring, and debugging platform — valuable for both testing and production incident analysis
• Cypress/Playwright with Synpress: Synpress extends Playwright/Cypress with MetaMask automation support for Web3 frontend testing

VTEST’s Approach to Blockchain Testing

Blockchain application testing requires a combination of smart contract expertise, Web3 frontend automation capability, and security testing depth that few generalist QA teams possess. VTEST works with blockchain development teams to design and execute testing programmes covering all five areas described in this post — from smart contract unit testing and fuzzing through Web3 frontend automation and security review. If you are building or maintaining a blockchain application and want independent quality validation, get in touch to discuss what’s needed.

 

In the vigorous process of testing software, if one doesn’t work with proper planning and efficient shortcuts, the whole thing can be chaotic. Reporting the bugs and errors found after the execution of the test is one of the crucial steps in the whole process. Communication wise it is one of the prominent parts of the process.

The correction of the bugs by the developers solely depends on the way the bugs are reported in the bug report. The report needs to be concise and should convey the information effectively.

Here, we discuss all the elements of creating an effective Bug report.

Defining Bug and Bug Report

A Bug in software is an error in code due to which an unexpected effect takes place in the behavior of the software.

Most bugs arise from mistakes and errors made in either a program’s design or its source code, or in components and operating systems used by such programs. While some are caused by compilers producing incorrect code.

The testing team detects these bugs and reports it to the developers’ team to take corrective measures. This reporting is done by a document called a Bug report.

The bug report is a document produced by a tester for a developer which consists of the information related to the bug in question and necessary steps and data about the reproduction of the bug.

Difference between a Good Bug Report and Bad Bug Report

To make it easy for you, we created a concise table to understand what are the differences between a good bug report and a bad one.

Good Bug Report: Pointers

Every bug report might have its conditions but some factors are to be considered while writing a good bug report. Below are some aspects of it which can be included in it.

• • Reporter: Your name and email.

 

• Product:

 

• Name of the application in which you found this bug.

 

• Version:

 

• Version of the application if any.

 

• Components:

 

• Components of the application.

 

• Platform:

 

• Name of the platform in which you found this bug, such as PC or Mac.

 

• Operating System:

 

• Operating system in which you found this bug such as Windows, Mac OS, or Linux.

 

• Priority:

 

• This state how important the bug is and how urgent the bug should be fixed. You can do it by assigning values like #P1 to #P5. With ascending from most important to less.

 

• Severity:

 

The effect of the bug in the application. What the bug is doing to the application and when and how is it affecting. There are different ways in which it can happen,
• • Blocker: Restricting for further testing
  • Critical: crashing of application
  • Major and Minor: loss of functions
  • Trivial: Needs UI improvement
  • Enhancement: Need a new or addition of a feature

 

• Status:

 

• The status of the bug. In the process, verified or fixed.

 

• Assign To:

 

• Information about the developer who was accountable for the bug in question. If you don’t know this, Specify the email address of the manager.

 

• URL:

 

• URL of the particular bug found in the application.

 

• Summary:

 

• A summary of the report is not more than 70 words. Add:
  • • Reproduction Steps: Precise steps to reproduce the bug.
    • Expected Result: The expected result of the application.
    • Actual Result: Actual result obtained while testing.

 

• Report Types:

 

This is an optional mention.Various types of reports can be mentioned like Coding error, design error or documentation issue, etc.

Bug Report: Important features

Some of the prominent features of the bug report are listed below. Make sure to add these to your report.

 

• Bug ID:

 

• To make it more accessible, you can assign a number to each of the bugs which will be unique of its own. By doing this you can easily check on the status of the bug anytime, anywhere.

 

• Bug Title:

 

• Giving a title to the bug can help easily guess what the bug is about. A keyword of sorts. It should be concise, easy to understand, and relatable. The developer should quickly catch up on it and it should smoothly convey the crux of the bug.

 

• Environment:

 

• It should be mentioned that in which environment the bug is found by the tester. It saves a lot of work of the developers and makes it easy for them to access the bug and solve it in the respective environment and/or platform.

 

• Description:

 

• This is the main part. All the information about the bug should be included in the description. This should be precise and informative and not confusing.It is a good habit if you report every bug separately as the confusion gets lessened.

 

• Steps of Reproduction:

 

• Accurate information about the bug and the proper steps about how to reproduce the bug should be mentioned here. This information is helpful to the developing team. Every step should be precisely specified in this section.

 

• Proof:

 

Some sort of proof or demo should be given in the report that should prove the developers that the bug you mentioned is valid and real. Taking a screenshot or recording the screen might be helpful.

Tips

Write the bug report as soon as you find the bug. Don’t procrastinate as you might forget things later.

• Reproduce the bug yourself 3 to 4 times. This will help in writing the reproduction steps and the bug will get confirmed.
• Write a good bug summary in the bug report. This way the developer can easily understand the bug and can work on it.
• Proofread your bug and remove unnecessary information.
• Do not criticize the developer for creating the bug and do not feel powerful upon finding it in the first place. It is not healthy.

We hope this blog helped you in some or the other way. You must write a fine bug report to enhance the whole process of software development and testing. As a tester, it is your responsibility to make the effort to convey the bug report suitably.

How can VTEST help

VTEST works efficiently and precisely towards the fineness of the application. For the test to get success without any obstacle, our testing team works in a way that is communicative, smooth, and easy to understand by all the members working on the project.

Good communication and fine grip over the language are the necessities to produce a good bug report. VTEST provides both of these qualities in a diligent manner.

Don’t just Test, Let’s VTEST

 

Related: Software Testing: A Handbook for Beginners

In the Software development process, the software can never be said as a fully developed output. A constant process of developing and testing newer versions and updates is a must for delivering a fine product.

After the primary development, software and applications need to test vigorously to detect bugs and sendthem again to the developers’ team to correct the code. Before releasing the product in the market, this happens several times.

To go through the above-mentioned process smoothly and efficiently, Drafting of Test plans is a necessary step taken by the testing team. It is a go-to guide of the test consisting of the objective, resources, estimation, schedule, and strategy of the test to be conducted.

It is an outline of the activities to be conducted while performing a test. It requires timely supervision and control of the testing team.

It is generally written by a member of the testing team who has a managerial sense. He/she needs to have full knowledge of the functionality of the system. The test cases are then submitted to seniors for review.

Significance

Let’s see why drafting a test plan is important.

It helps the team to understand and decide the variables involved in the process and anticipate the efforts required to authenticate the system. It also helps in executing a qualitative analysis of the software under different tests.

The document helps other developers and business managers to gain knowledge about the details of the tests.

It serves as a manual that leads testers throughout the process and allows them to follow the standards. The team can later review and use the plan again for scope, test estimation, test strategy, etc.

Now to the main part. How? Let’s see how to create a test plan for testing an application. Below are the 8 steps,

1. Product Analysis
2. Strategy Design
3. Interpretation of the test objectives
4. Outlining test criteria
5. Resource Planning
6. Defining test Environment
7. Estimation and Scheduling
8. Governing test deliverables

1. Product Analysis

For creating a test plan, first, one needs to know all about the product he/she is testing. A proper study of requirements and analysis of the system is the first step.

It involves several things like Client research, End users and their needs and expectations, Product delivery expectations, etc. Consider the following points.

• The intention of the system
• Usageof the system
• Users and Usability
• Development Requirements

The client can be interviewed to get more detailed insights or if the team has any doubts about the points mentioned above.

2. Strategy Design

Designing the strategy is one of the prominent steps in drafting a test plan. The test manager designs document here which is of high importance in the whole process. It consists of testing objectives and the pointers to attain the objectives by deciding the budget and several other variables.

Mandatory inclusions in this document are as follows:

• Scope of the test
• Testing type
• Document hazards and problems
• Test logistics creation

3. Interpretation of the test objectives

Interpreting and defining the precise objectives of the respective test is the building block of the process. The obvious objective here is to detect as many bugs possible and remove them from the software. To do this step, there are 2 sub-steps as follows,

1. Make a list of all the features and functionalities of the software. Include notes about its performance and User interface here.
2. Target identification based on the above list.

4. Outlining test criteria

Here a rulebook or standard for the test is made. The boundaries get decided. The whole process is supposed to play between this. 2 types of test criteria are supposed to be decided,

1. Suspension – Specifying critical suspension for a test. When this is fulfilled, the active test cycle is adjourned.
2. Exit – This criterion states a positiveconclusion of a test chapter.

5. ResourcePlanning

As in the name, here, one is supposed to plan the resources. To make a list and analyze and summarize all the resources required for the test is the gist of this step. This list of resources can consist of anything and everything needed. People, hardware, and software resources, etc.

This step is mainly helpful to the test manager to plan a precise test schedule and estimate the resource quantity more accurately.

6. Defining test Environment

Don’t get worried about the big word here. This ‘Environment’ includes a combination of software and hardware on which the testing is to be performed. It also includes other elements such as the user, servers, front-end interface, etc.

7. Estimation and Scheduling

Continuing from the earlier step, now the main task is to make an estimation and schedule of the testing process. It is a common practice to break down the estimations into small units and then noting the whole estimation while documentation.

While scheduling, many things are to be taken into account while scheduling the test such as Project estimation, Employee deadline, Project deadline, Project risk, etc.

8. Governing test deliverables

This final step includes all the documents, components, and tools that are developed for the testing efforts by the whole team. Most of the time, the test manager gives the deliverables at definiteintermissions of the development.

The deliverables consist ofdesign specifications, error and execution logs, plan documents, simulators, installation, test procedures, etc.

In Conclusion,

We covered the whole drafting of the test plan in these 8 steps. We hope that this will help you or your team to create the test plan. Remember, every software requires different specifications and requirements in the test plan. While making your plan, make sure you are considering all the factors proposed by your specific software.

How can V-TEST help

The executive qualities of the testing team of VTEST are its main benefit. Here at VTEST, we don’t have only geek testers who are new to the industry. We work professionally with ace testers who also have the necessary managerial skillset.

The whole process of testing at VTEST including the drafting of the test plans is as efficient as the abacus and as solid as a rock.

As they say, VTEST it! .

 

 

Related: Software Testing: A Handbook for Beginners

Artificial intelligence has been discussed in software testing for over a decade. But the AI being used in QA teams today is fundamentally different from the ML-assisted defect classifiers of five years ago. This post covers the current state of AI in software testing — the real tools, the practical applications, and what enterprises need to understand to use AI effectively in their quality assurance programmes.

The Evolution: From Rules to Reasoning

Early AI in testing consisted of rule-based systems and simple ML models — tools that flagged anomalies in test results, classified defects by severity, or optimised test selection using historical pass/fail data. Useful, but limited. They required large training datasets, months of calibration, and still depended heavily on human-written test scripts to function.

The introduction of large language models (LLMs) — GPT-4, Claude, Gemini, and the open-source models that followed — changed the paradigm entirely. For the first time, a system could read natural language requirements, understand code structure, and generate tests without being explicitly programmed to do so. This capability is now embedded in mainstream developer tools and has moved from research projects to production QA workflows.

Core Applications of AI in Software Testing Today

AI-Powered Test Generation

QA teams can now describe a feature in plain English — or provide a user story, an API spec, or a code diff — and ask an AI assistant to generate a full suite of test cases including positive, negative, boundary, and edge case scenarios. GitHub Copilot, Cursor, and dedicated QA AI tools like Qodo and Octomind do this natively within the development environment.

The impact is significant: test design work that took a skilled QA engineer a day can now be drafted in minutes. The engineer’s role shifts from writing tests to reviewing, curating, and augmenting what the AI produces.

Intelligent Test Execution and Optimisation

Running every test on every build is wasteful. AI-driven test orchestration analyses the code changes in a commit and predicts which tests are most likely to detect failures from those specific changes. Only those tests are run in the fast CI pipeline; the full regression suite runs nightly. Teams using this approach have cut median CI pipeline times from 40+ minutes to under 10 minutes while maintaining equivalent defect detection rates.

Self-Healing Test Automation

The maintenance burden of UI test automation has historically been one of the biggest obstacles to scaling it. Every UI change — a button moved, a class renamed, a step added — breaks existing locators and requires manual script updates. AI-powered self-healing tools (Healenium, Testim, Mabl, Waldo) detect broken element locators at runtime and automatically identify the best matching element using contextual reasoning. Scripts stay green through UI changes without manual intervention.

Visual AI Testing

Computer vision models compare UI screenshots across devices, browsers, and resolutions at scale. Unlike pixel-diff tools that flag every rendering variation as a failure, AI visual testing tools (Applitools Eyes, Percy, Lost Pixel) learn which variations are meaningful visual regressions versus acceptable differences. This makes cross-browser visual testing practical at the speed of CI/CD.

AI in Performance and Security Testing

AI is extending into non-functional testing domains. In performance testing, AI agents dynamically adjust load scenarios based on real-time system telemetry, identifying stability thresholds more intelligently than static ramp-up scripts. In security testing, AI-powered fuzzing tools generate adversarial inputs far beyond what rule-based scanners produce, discovering novel vulnerabilities in APIs and web surfaces that traditional DAST tools miss.

Agentic QA Systems

The most advanced current application is agentic testing: AI agents that orchestrate the entire quality lifecycle autonomously. An agentic QA system can be given a feature brief, spin up a test environment, generate test scenarios, execute them, analyse failures, attempt automated fixes, and produce a quality report — all without a human directing each step. This is not a future concept; early production deployments of agentic QA systems are running at enterprise scale today, though most still operate under human supervision at key decision points.

What AI Does Not Replace

Despite the rapid capability gains, there are important limits to what AI handles well in software quality:

• Exploratory testing: Finding the bugs that don’t fit any script requires human curiosity, domain knowledge, and the ability to notice that something “feels wrong” even when it technically passes. AI is not good at this.
• Usability and UX judgment: An AI can verify that a button exists and is clickable. It cannot tell you whether the user journey is intuitive or the copy is confusing. Human evaluation is irreplaceable for experience quality.
• Test strategy: Deciding what to test, what not to test, and where to focus quality investment requires business context, risk judgment, and stakeholder communication that AI cannot own.
• Validation of AI-generated tests: LLMs produce plausible-looking but occasionally incorrect tests. A human QA engineer must review AI output critically — the skill shifts from writing to evaluating.

Integrating AI into Your QA Practice: A Practical Starting Point

For organisations that are evaluating where to start with AI in testing, the highest-ROI entry points are typically:

1. AI-assisted test case generation for new feature development — start with LLM tools in the IDE and build review workflows around AI output
2. Predictive test selection in your CI pipeline — measurable CI time reduction with minimal disruption to existing tests
3. Self-healing UI automation — immediately reduces maintenance overhead if you run a Selenium or Playwright suite

Full agentic pipelines are appropriate for teams that have already matured their conventional automation practice and have the engineering capacity to evaluate and govern AI system outputs rigorously.

VTEST and AI-Driven Quality Assurance

VTEST has been embedding AI tools into client QA engagements since 2023. Akbar Shaikh, our CTO, leads the technical direction on AI adoption — evaluating tools, designing integration patterns, and ensuring that AI augments rather than obscures the quality signal. We work with enterprises across domains to implement AI testing capabilities that are governed, measurable, and genuinely improve release confidence — not just impressive in a demo.

If you want to understand which AI testing tools are mature enough for your stack today, and how to build the internal capability to use them well, get in touch.

Related: Agentic Testing: The Complete Guide to AI-Powered Software Testing