Have you been significantly investing in quality assurance testing? Yes? But how do you ensure that your QA test cases are still relevant in this era of artificial intelligence and machine learning? It is very important for testers to adopt AI QA testing processes to ensure that even the complex elements of the application are thoroughly tested before they are forwarded to the end users.
AI QA promises faster feedback loops, more accurate test coverage, and deeper insights into the software quality issues. Therefore, the proper use of this workflow will massively improve the quality and productivity of your environment.
Still confused about AI QA testing? Don’t worry! We’re here for you. Our article will help you understand the future of QA in the age of AI while understanding various AI-driven tools to improve your software testing processes. We will also understand some of the major challenges of this process, along with the best practices that can help you overcome these issues.
What Makes AI Transformative for QA?
With artificial intelligence, you can allow your testing machines to perform tasks which typically require human intelligence. These tests can include pattern recognition, learning from experience, and making predictions in QA.
To further understand the role of AI in QA testing, let us divert our attention to some of the major areas that can benefit from these inclusions:
Intelligent Test Case Generation
You can use AI to study the application’s behavior, user interactions, and historical bug data. Based on all the information that you will gather from this process, AI can automatically generate the test cases for every possible test scenario. You can also train the AI models to explore the application state, identify edge cases, and generate tests which a human tester will mostly overlook.
Adaptive Testing
A very important factor that you must remember is that traditional test scripts will be brittle. So, if the user interface or any associated logic changes, large parts of the test suite will break, leading to a completely devastating testing experience. On the other hand, AI-powered testing tools will adapt to UI changes by using computer vision and machine learning algorithms.
The result? You can massively reduce the maintenance overhead and also improve the scalability of the overall testing infrastructure.
Predictive Analytics
AI will analyze the different trends and code commits over time. It will then use this information to detect which components are most likely to fail in the future releases. The AI teams will use these valuable insights to guide their efforts to areas of highest risks. You can also use this power to allocate resources efficiently and catch the bugs before they escalate into serious concerns.
The most important benefit of this process is that it will help you ensure that the basic functionality of the application’s infrastructure is always functional, even in a worst case scenario.
Natural Language Processing
In most of the application development industries, the test requirements and test cases are documented using natural language. You can use AI-driven NLP tools to interpret these documents and extract the relevant information. AI can also use this information to suggest new test areas to further improve the proper functioning of the application.
The proper implementation of this approach will ensure that you are speeding up the testing requirement validation process and also the test planning phase.
Self-Healing Test Automation
Are you conducting automation testing? Yes? Then, you must have realized that one of the major challenges is that even a small change in the code can result in test failures, even if the primary application is defect-free.
The solution? AI-driven self-healing automation tools can dynamically update the locators or test logic when minor interface elements change. This approach will hugely reduce false positives and the need for manual intervention in the software testing process.
AI-Driven Testing Tools and Techniques
The current market will offer you multiple AI-driven testing tools and techniques to improve your QA processes. To shed more light over this segment, we have mentioned some of the major offerings below:
Model-Based Training
AI systems can build models of how an application should behave based on its requirements, user stories, or usage patterns. After building this model, artificial intelligence will generate test cases from these models.
You’ll also have access to tools that implement these model based training to systematically move all states of the application while achieving higher test coverage without the need for detailed handwritten human tests.
AI Cloud Testing
Real device testing is one of the most important aspects of the modern software development process. This is because with real device testing, you can understand how the application’s behavior changes when it is exposed to various physical parameters of the device. There are various AI testing tools that can help you achieve this process over cloud platforms.
The advantage? Cloud platforms allow access to thousands of different devices, including legacy ones, without the requirement of setting up a million dollar onset device lab. Let us understand more about this process by using the example of LambdaTest:
LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 5000+ real devices, browsers, and OS combinations. It will also provide access to various advanced features like comprehensive test reporting, native bug tracking, and live test activity logging.
Visual Testing
There are various tools equipped with computer vision to detect visual anomalies like misplaced elements, incorrect fonts, or overlapping text in user interfaces. The AI system will compare screenshots or rendered objects with known baselines to identify even the most subtle UI regressions.
You can use this approach to create more robust testing scenarios compared to the traditional manual pixel comparison approach. The use of artificial intelligence also improves the reliability of the testing process as it can incorporate factors like acceptance, tolerance levels, or anticipate how small layout changes may still be correct.
Log And Error Analysis
QA and DevOps teams often rely on multiple system logs to identify issues within the infrastructure of the application. AI-driven log analysis will detect patterns or abnormalities that signal potential defects or performance degradation within the source code.
There are also additional tools which can integrate with monitoring systems to alert QA teams when suspicious patterns appear in real time. This approach will close the gap between testing and observability in a production environment.
Test Maintenance Automation
Finally, with the evolution of your application, you will have to deal with large automated suites. In such a scenario, artificial intelligence will automatically refactor the test scripts when it detects changes to the source code of the application.
This approach will help you to reduce the time the QA engineers will spend updating the test scripts manually. You can also use it to identify redundant or overlapping tests to recommend their removal and streamline the overall testing suite.
Does your app go through constant updates? AI will help you in this regard as well as it can automatically update the test suites depending on all the new elements added or the old elements that you have removed from the user interface of the application.
Challenges and Pitfalls of AI in QA
Despite the promises of AI to completely change the quality assurance process, you must remember that it will never become a magic bullet. So let us divert our attention to some of the major pitfalls that you can witness while implementing AI in your QA testing processes:
- While Al will help you reduce the manual involvement in the testing cases, you can never blindly trust it. This is because if you do so, you will witness missed edge cases or misinterpretations of testing requirements. Therefore, you must implement human oversight to confirm that AI insights will align with real-world functionality.
- Since AI models will learn from your historical data, it will develop bias if the data is biased or incomplete. You can witness these biases in predictions, test scenarios, or bulk classifications. Therefore, you must ensure diverse and representative test training data to actively monitor AI outputs.
- If you’re using AI testing tools that involve advanced machine learning, you will see that it is very complex to set up this process and also maintain it continuously. Moreover, if you are working for a smaller organization with limited technical resources, you will struggle to implement these advanced processes as you will not have the right infrastructure or support.
- There will be certain AI-driven QA tools which will require access to sensitive information like user data or property code repositories. While granting these data, you must ensure that the access complies based on various security standards and regulations.
- At the earlier stages of AI QA testing, the system will learn and refine itself. During this training period, AI can generate false positives like flagging issues that are actually not defects or false negatives, which is the exact vice versa. So, as a team, you must be prepared to handle these inaccuracies in the short term.
- To properly use the AI solution, you will need a computational resource for training and execution. While cloud-based platforms can help scale the resources on demand, the subscription costs for these platforms will become a barrier for small organizations or individual creators.
The Bottom Line
Based on all the factors that we have put forward in this article, we can easily say that AI QA testing is the future of software development. By automating all the tiresome and repeated testing processes, AI can help the QA professionals to focus on strategic, creative, and user-centric aspects of the software development and testing process.
This inclusion will also allow the individual creators to improve their roles and start becoming major contributors to not only the software reliability process but also to user experience, ethical AI deployment, and continuous Business Innovation.
If your organization embraces AI thoughtfully by balancing ambition with practical constraints, you will find yourself at the cutting edge of software quality. You’ll be able to deliver apps quickly with fewer defects and tailored experiences depending on the requirements of your target users.
Finally, we would like to say to the testers that the road ahead for AI QA testing might be complex, but if you’re prepared to adopt AI with an open mind and strategic vision, you will have a very exciting future both in terms of individual growth and business development.
