How AI Is Used For Testing Modern Software

Is traditional rule-based testing still the most advanced testing process in your environment? We are sorry to say you are massively falling behind! This is because various advanced processes like artificial intelligence and machine learning algorithms have completely changed how you are testing AI capabilities on modern applications.

If you properly use these integrations, you will be able to have more efficient, fast, and accurate test cases. It will also help you to fill the gaps within your test environment, create smarter, faster, and more adaptive testing workflows.

Don't know how to use AI testing on modern applications? Don't worry! We are here for you. Our article will help you understand how AI can change your software testing practices, along with its benefits, and challenges. We will also help you with some of the best practices that can assist you in these workflows.

Why Use AI In Software Testing

Since DevOps and agile methodologies have become the standard practice for creating modern applications, the entire industry has massively reduced the time period for creating modern applications, faster. However, due to the rising complexity of these apps, this reduced time period can be a major challenge.

This is because you have to create various ways to implement rapid, reliable, and continuous testing that can ensure that the application performs as intended after the deployment process. Depending on these thoughts, let us divert our attention towards some of the major scenarios that justify the use of AI in software testing:

• Modern software systems use hundreds of microservices and user interface elements to improve the end-user experience. When it comes to verifying the functioning of these elements, it can be a massive testing process as you need to implement an exhaustive testing approach.

• Let's face the truth, manual regression testing can never keep up with the requirements of continuous deployment and continuous integration pipelines. This means that if you are using these processes, you can no longer implement the testing and development process at the same time.

• With the power of artificial intelligence, you can scan multiple data sources, data points, and structures to grasp a huge amount of test and production information. Depending on the information that you will get, you can convert them into actionable insights to improve the quality of the application.

• If your application goes through frequent user interface changes, it can break the functioning of the entire hard-coded test script. On the other hand, using the capabilities of artificial intelligence, the system can automatically adapt to these changes without the involvement of any human tester.

Tests That Can Benefit From AI Integration

Let us now divert our attention to some of the major areas of the application development and testing processes that can benefit from the integration of artificial intelligence and machine learning:

Test Case Generation

You can use the power of artificial intelligence to go through the user behavior, code changes, and requirement documents. Depending on the information that you will extract from here, you can automatically create the test cases using the power of natural language processing.

The main concept of this approach is to understand the test scripts written in plain English language and then automatically convert them into a language or format that can be understood by the testing machine for running the test cases.

Defect Prediction

Machine learning models will help you to use the historical defect data and code metrics like complexity and churn to predict which models of the application are likely to contain bugs. Depending on these useful insights, you can automatically allocate all your testing resources to these high-risk areas to ensure that at least the basic architecture of the application is functional in all instances.

Test Optimization And Prioritization

Using the power of AI, you can understand all the past test runs and identify the repetitive test cases. This approach will be very important to speed up the overall test execution process and also improve the quality of the high-impact test cases. With this approach, you can further implement the following steps:

• Perform algorithm clustering and reinforcement learning so that you can automatically prioritize the most sensitive test cases.

• Perform A/B tests on the entire feedback loop to improve the accuracy of not only the test cases and test reports but also the resolution steps.

Self-Healing Test Scripts

Another major advantage of artificial intelligence is the use of self-healing test scripts. This is because modern applications undergo several changes to add or remove features, which can turn the traditional code-based test scripts into highly fragile instances.

But with AI-driven systems, you can dynamically look at the elements using multiple identifiers and heal the test strips without the involvement of any manual tester. For example, you can also use dedicated tools like Mabel and Testim to access these self-healing capabilities.

AI Cloud Testing

Another modern aspect of AI in testing is the use of cloud testing platforms combined with AI tools for developers.Can you imagine releasing a software without actually verifying how its performance changes when it is used on physical devices? Factors like a broken display, user interaction errors, or a low battery can have a catastrophic impact on the performance of these apps.

Many testers often avoid real device testing due to its huge expenses and the massive hassles of not only setting up the test environment but also keeping it updated, considering the thousands of devices that come to the market every year.

What if we told you there is a better alternative which will help you achieve the same test reports while completely eliminating the physical setup process? It's simple! You need to start investing in AI developer tools 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.   

This platform will provide you with various advanced features like native bug tracking, live activity logging, and also comprehensive test reporting for easy test debugging. 

Challenges in AI Testing

Depending on all our previous discussions, you must think that AI is a completely perfect measure that does not have any form of shortcomings. Right? Actually, no! Even with an advanced architecture, AI testing also has its own share of shortcomings. To shed more light over this segment, we have mentioned some of the major ones below: 

• The process of training and preparing the AI model is highly dependent on huge sets of training data. Therefore, if you use low-quality or insufficient data, the AI model might not be capable enough to perform accurate test cases.

• The initial setup process for AI-based software testing can be highly complex. This will be even more significant if you're a new tester who is just transitioning to this industry. The companies have to invest in dedicated training workshops and seminars so that the testers can get accustomed to all the features of this new workflow.

• While using AI models, you will realize that many such models work as black boxes. This means that the justification criteria or the decision-making algorithm might not be clearly visible to the human testers. It will become very difficult to explain why a certain defect was predicted by the model.

• The process of acquiring all the relevant licenses and setting up the infrastructure will be highly expensive for an AI model. This can become especially high if you're not using open-source tools.

• Due to the advanced architecture of AI, many testers and developers can get the idea that it can completely replace the involvement of manual testers. But the reality is that you should always have some form of human oversight during the test execution processes.

Best Practices For AI Software Testing

Finally, let us go through some of the best practices that we highly recommend you to add while using AI in a software testing process. With the proper use of these practices, you can not only improve the productivity of the testing environment but also help boost the quality of the application that you are working on:

• It is very important to have a clear idea about the application that you will be creating. This is because with this knowledge, you can create a very elaborate action plan to understand all the target devices, acceptance criteria, and other factors influencing your AI testing environment.

• As we already mentioned earlier, AI can never replace the involvement of human testers in a test bench. So, the main focus in this segment should be to maintain a proper balance between human expertise and AI capabilities. A great starting point would be to use AI for all the repetitive test cases and human experience for the complex testing scenarios.

• Since the concept of AI is completely new to the market, we would not recommend you to completely transition your entire testing environment to this process. Instead, you should start with a non-critical test case and monitor its KPIs. Depending on the success that we achieve in this process, you can scale accordingly. This approach will also help you receive the stakeholder approvals regarding the shift.

• You should watch out for all the upcoming trends and innovations in the segment of artificial intelligence and machine learning. This is because if you can quickly adopt all these practices, you will be able to provide an experience to our customers that was previously unimaginable with the traditional practices.

• You should maintain thorough documentation throughout the AI testing processes. These documentations will consist of the test reports, the bugs detected and all the errors found. Using this approach, you can not only avoid all the previously known errors but also ensures that there is a general understanding about the current status of the application testing process.

The Bottom Line

Based on all the factors that we went through in this article, we can easily say that although AI will not completely replace the involvement of manual testers, it will help improve your capabilities in software testing.

With AI, you can access intelligence, adaptability, and speed, all of which are very important to improve the software quality while enhancing its accuracy. By using all the strategies and best practices that we have mentioned in this article, you can ensure that you are giving the best experience to your customers that was previously impossible.

You must remember that due to the increasing reliance on automated systems and intelligent automation approaches, using AI in software testing is one of the most crucial steps that you must start investing in right away.