ai qaAre you still manually testing the stability and accuracy of your test cases? It is very clear that you are massively falling behind, considering the constantly changing requirements of the app testing industry. So what’s the solution? It is very important to start transitioning to ai qa test cases.
With the use of AI QA test cases, you can use a lot of highly advanced features like predictive analytics, natural language processing, and machine learning algorithms. The advantage, you ask? Ensure that your test cases are not only accurate and scalable but also adaptable to all the changing requirements of your application.
Not familiar with the AI QA testing? Don’t worry! We are here for you. Our article will not only help you understand the basics of this process but will also go through some of the best strategies and practices to ensure the stability and accuracy of the implementation.
What Is Predictive Analytics In QA
Before we go into any other detail, we must understand how advanced predictive analytics can influence your quality assurance testing processes. It is the approach of using statistical algorithms, machine learning knowledge, and historical data to understand the future outcomes of the testing process.
In simple terms, you can use this approach to find out potential failure points, optimize the test coverage, and prioritize the testing efforts depending on risk. The ultimate goal for this implementation is to improve the testing efficiency and also the long-term reliability of the application that you are working on.
Why AI in QA
Since the main goal of this article is to spread awareness about using AI in QA test cases, it is only fair that we justify that why should you even consider using AI in quality assurance testing processes. To shed over this segment, let us divert our attention towards some of the most important technologies that you can use while using AI QA testing:
- With machine learning algorithms, you can analyze the historical defect data to understand the future defects. It will also help you gain substantial information about all the critical areas of the application that are most probable to develop an error.
- You can use natural language processing to turn your business requirements, user stories, and other meeting notes into test scripts that can be understood by the system. Therefore, it will play a very important role in reducing the technicalities of your automation testing process.
- You can use computer vision to finally introduce automation in your visual test cases. With this approach, you can use the power of artificial intelligence to analyze the proper placement and functionality of all your UI elements. It will also help you to easily locate the elements even after changes to the application infrastructure.
- Finally, you can implement reinforcement learning to adapt and optimize the testing strategies over time. The main goal of this process is to change the test cases so that they can incorporate all the elements that you will be adding or removing from the core infrastructure of your application.
Compared to all the above technologies, you can consider the predictive analytics process as more of a strategic approach to optimize the test cases because it will use a combination of multiple tools to achieve the outcome.
What Does Predictive Analytics In QA Consist Of
To gain more understanding about the use of predictive analytics in your QA test cases, let us divert our attention to some of the major components that you must include in this entire workflow:
Data Collection
You must remember that to ensure the accuracy and proper stability of a predictive analytics process, you must use high-quality data throughout the training and implementation process of the AI models. This is because your QA testers can use various forms of data sources like test case execution logs, code reports, user behavior analytics, and the requirements documentation.
After you feed all the above information to the machine learning model, it can find patterns and make predictions about future issues or the impact of the testing case. Depending on the reports that it will generate, you can redirect all your testing efforts accordingly.
Risk-Based Test Prioritization
Predictive analytics will also allow you to identify high-risk areas of an application by understanding the frequency of the changes, historical defect density, and the developer activity.
So if you focus all your testing efforts on these high-risk areas that have been identified by the machine learning model, you can find critical bugs at the earlier phases of the development cycle, even before they can become a serious concern.
This approach will not only help remove repetitive testing but will also help improve the confidence in the quality of your application and the release cycle as a whole.
Defect Prediction Models
You can use the historical defect data and feed it to the AI model so that it can predict which module is most likely to AmbitoDigital, the nature and the severity of the failure, and the probability of regression defects to arise within the core infrastructure of the application.
The main goal of this model is to help you to efficiently allocate your testing resources and reduce production defects. It will also help you to reduce the hassle every time your application undergoes any major update.
Test Case Optimization
The final major component of predictive analytics is test case optimization, including the test case selection, test case reduction, and test case maintenance processes.
- By implementing test case selection, you can choose only the most impactful tests to ensure that there is at least one test case allocated for every element present on the application.
- The test reduction helps you eliminate all the repetitive test cases and those test cases that can contradict each other. This is a very important step to ensure the accuracy of test cases and eliminate flaky test reports.
- The final phase is test maintenance to continuously update the test suite depending on various new predictions and also the advancements within the core infrastructure of your application.
Factors like this will play a very important role in improving the economics of an application, including the returns on investments.
How To Implement Predictive Analytics In QA Testing
Excited about using AI QA testing? Let us now divert our attention to some of the major steps that you must follow to efficiently implement this process:
- The first step is to collect and clean the QA related data from multiple sources, like Jira, Jenkins, and Selenium logs. It is also important to ensure that all the data has adequate timestamps, results, defect logs, and the required code metrics.
- After you have collected your data, you can convert them into meaningful features like code data, test case age, execution duration, and the priority metrics.
- The next step is to choose the model to implement AI QA testing within your software delivery and testing cycle. ML models like Random Forests, Neural Networks, and Gradient Boosting Machines are the perfect option for this approach.
- Now that you have the required data and also your model, it is time to train your AI model with the data that you have collected in the first step. Proper training is very important to ensure the accuracy, precision, and other privacy compliances within the testing process.
- The final step is to integrate the entire AI QA testing with your continuous deployment and continuous integration pipelines. This approach will help you to run the intelligent test selection or risk-based test automatically without the involvement of a human tester.
Apart from the above steps, you should also have an additional requirement where you are maintaining a thorough documentation of all the tests that you are running and the results that you’re achieving. This documentation will keep track of all the known errors and help to avoid them in the future circumstances.
Best Practices For AI QA Testing
Finally, let us divert our attention towards some of the best practices that you should include within your testing workflow while you’re integrating AI QA testing. Why? This will help you improve the quality, scalability, and efficiency of your entire testing workflow:
- Since the concept of AI in software testing is literally new, we would never recommend you to directly migrate all your test cases to this workflow. So, what’s the alternative? You should start small with a non-critical test case and closely monitor the success rate. Depending on the results that you achieve, you can scale accordingly.
- AI is a constantly changing industry. It is very important to look out for all the upcoming trends and innovations in this segment. Whenever you see any relevant addition, you should quickly integrate it into your testing workflow to further improve the quality of your application.
- However advanced AI becomes, it can never completely replace the requirement of a human tester in the loop. The best approach is to use AI for the repetitive test cases and still rely on human experience for all the complex and unique areas of the testing process.
- While you’re investing in ai testing, it is equally important to ensure the efficiency of your application on real devices. You can easily implement this process without actually setting up a real device lab with the help of cloud-based platforms like LambdaTest. LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and 5000+ real devices.
Apart from all the practices that we outlined in this article, it is very important to understand that there are certain strategies and best practices that need to be customized depending on the specific requirements of the application that you’re working on. To achieve this process, you must have a clear understanding about the requirements of the application and the end goal.
The Bottom Line
Based on all the areas that we covered in this article, we can easily come to the conclusion that AI predictive analytics in quality assurance is helping convert your test cases into a strategic, data-centric, and accurate workflow. By implementing all the processes and steps that we have mentioned in this article, we can not only ensure the proper implementation of this workflow but also the optimal utilization of your available resources.
Ready to get started with the AI QA testing? Focus on strong data pipelines, choose interoperable models, and integrate predictions within your development workflow. With these considerations, you can constantly provide the best experience to your customers to build a positive reputation and a strong user base.