Is AI the future of Testing?

Software development has already undergone an important journey from its beginnings to test automation and continuous testing. As time progresses, however, it is certain that testing will have to evolve as well. With digital transformations and the drive towards DevOps, automated testing is now at the heart of software testing and has taken the lead in the development process.

Artificial Intelligence (AI) seems to be the future of testing. AI has created high hopes in software testing and test automation and the advancements in AI allow organizations to transform their processes and make progress. With a future driven by robotics, IoT, and quantum computing, AI seems to be the key to change.

 

AI in Testing

AI in Testing can be used in different ways. One straightforward case is using AI in generating unit tests. AI can help with static analysis of the software in order to know which areas of the code unit tests haven’t treated. The tool can then use this information to create unit tests for uncovered code and provide input for these unit tests. With an AI-driven tool, it makes generating unit tests easier, as these tests don’t require any setup or interactions with other components.

In the future, AI will be able to update these unit tests as soon as the source code changes, and thus, maintain unit tests. This also means that developers will spend less time on test maintenance.

But the AI requires training to know what pieces of code need stubbing or mocking in order to isolate them. Thus, the AI needs to learn to determine dependencies – a process that takes a lot of time to teach. Once the AI has been trained properly, it will be able to save developers so much time in unit test creation.

For Application Programming Interface (API) Test generation, the AI has to understand the patterns and relationships in the different API calls made while exercising the UI. Following that analysis, the AI can create a series of API calls and provide parameters to test. Moreover, AI can also generate more advanced patterns and inputs for testing the API by studying the behavior of users.

AI and Machine Learning (ML)’s tools can enhance the authoring, execution, and maintenance of automated tests. These tools emphasize end-to-end testing and user interface testing; and the more they are used; the smarter they become and in time, increase the stability of test suites.

Benefits of AI in Testing

AI has a lot to give to Testing. With AI, Testing becomes error-free and simple. With the new algorithms and tools, software developers spend less time writing tests and examine the data. AI has the ability to sort through log files and improve the accuracy of specific programs in order to save time. The data generated give developers an insight into what areas of testing need to be fixed, hence spending less time running the actual tests and speeding up the development cycle. The new algorithms also prevent future errors or complications thus improving the efficiency of the tests.

Moreover, AI helps optimize the test suite by clearing the backlog and enable focus on the tests. AI can identify the redundant and unique test cases, hence, eliminating the duplicates and improving traceability.

AI is also able to identify defects early on in the development process and avoid it in the future. By finding faults quicker, developers can prioritize regression test cases based on the identified errors.  Furthermore, by using the information generated by the AI, testers can detect and eliminate bugs as soon as they appear. AI is constantly testing to clear-out excessive bugs from programs.

Finally, AI can be used to analyze test results, identify flaws, and predict the quality. It uses this data to predict the key parameters of procedures and authorize the best course of action. Hence, it will be able to spot the crucial, critical, and less obvious faults.

All in All, AI generates more advanced test cases that enhance the quality of the software and, in the long run, help save time and money as it reduces the testing cycle.

However, AI cannot be applied to every type of testing. Indeed, testing types such as integration tests, which are harder to generate and require more advanced setup and complex flow.

 

Conclusion

In the future, AI will have a significant impact on the quality function with benefits that go from superior quality to a quicker time to market, better optimization, coverage, traceability, and important savings on the costs of development. However, AI has still a long way to go to be completely ready to take over in software testing.

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