The Role of AI in Test Automation

By adopting an agile methodology, firms are producing new software products like never before. Gone are the days when products were built over a period of months. Development teams need to build, test and validate products efficiently to address the current market needs. The shift from manual to automated testing improves efficiency and accuracy but artificial intelligence (AI) has a lot more to offer. When continuous testing is combined with AI, it changes the approach to software testing. An automation testing company helps firms achieve speed, accuracy, and reliability.

The Contribution of Artificial Intelligence in Software Testing

Automated tests reduce manual efforts and the potential for human errors. Developers write automated test cases that machines can run and aim at achieving the desired app behavior. Automated testing allows development teams to check the applications’ aesthetics and reduce manual efforts on repetitive tests. This is what the industry has expected and needs to meet the current market demands. So testers using continuous testing need to back it with AI capabilities to meet the delivery needs.

AI enables automatic creation of test cases. It not only reduces the amount of load on manual testers but also streamlines test cases. AI will also have a great impact on the maintenance of generated test cases. While products evolve and grow, AI will also modify the tests. Testers will be able to identify the testing needs and adapt the end goals with AI.

Test Case Generation with AI

Firms have huge data from various sources that include log files, user actions, and testing results. Automation testing companies provide AI techniques and resources that testers use to gather, examine and test user data and look for actionable patterns. The first and foremost step is to choose stable production data. It means that testers should remove any data generated by malicious activity. It will be used by the AI to generate tests and it can be useful in integration testing.

AI Capabilities Leveraged in Continuous Testing

Using AI in continuous testing can also recommend that certain tests should be performed. For instance, when video data on user usage patterns are used in AI testing, it can uncover common patterns. These work as heatmaps and can be used to create unit tests for developers. When moving towards AI, it builds the quality coverage required for automated testing. Developers no longer need to guess how to test the system, they have AI capabilities to do so.

Maintenance of Automated Tests

Testers usually estimate the effort involved in automated test creation when preparing for test case automation - they fail to include the cost and time involved in its maintenance. If testers fail to update a suite of tests, they become obsolete. So it is important that their testing efforts are not wasted.

Conclusion

Before sending a product into production, it is recommended to make the desired changes to it. With each release comes a new user interface (UI) that leaves some test cases useless. Ideally, testers would have the details of the required changes, but now they need to be done faster than before. Firms hire an automation testing company to ensure they are on the right track when moving from manual to automated testing.

Ray Parker is a Senior Marketing Consultant with a knack for writing the latest news in tech, quality assurance, software development and testing.