5 Ways AI is Changing Test Automation

Software testing has evolved a lot since the time when the waterfall model was used. All the work was done in a sequential manner and only after the development phase was complete the testers used to test the product. Testers used to find bugs but a lot of time and energy was wasted in the process to rebuild and code again.

Now companies are using an Agile model where the main goal is to find the bugs in continuous development, fix them quickly and release the app faster. There is a need to improve the automated testing process to complement the manual testing. More emphasis has been given to CI, CD, and DevOps to make the software development effective.

There has been a considerable change in the functioning of testing tools and test automation frameworks. The most important change is the introduction of AI in a test automation strategy.

According to G2Crowd, AI-powered bots are expected to cut business cost by $8 billion by 2022. Testing bots are already empowering automation testing and will play a major role in reducing the time and effort spent in mobile app testing.

Let’s have a look at how AI is breaking new ground for test automation.

1. Running automated tests that matter

It’s not a good strategy to run your entire test suite due to a very small change in your app that you couldn’t trace. You are probably already generating a lot of data from your test runs if you are doing continuous integration. But it will take a lot of time to go through the data and search for common patterns. So you need to know if you make a small change in code then what is the minimum number of test you need to run to figure out if the change is needed or not.

2. Reducing maintenance and eliminating flaky test

We can run several automated tests on a daily basis to ensure the functionalities of the app are still stable. Although, if we find out that half of this test failed. In that case, we would need to spend a lot of time to troubleshoot the failures and investigate the cause. Then there is a need to find ways to fix the failures and then work on the changes.



software maintainance

Using AI we can avoid issues and start detecting issues in the test before they even occur. So instead of reacting to it, we can proactively fix tests. AI can figure out which tests are stable or flaky based on the number of test runs and it can tell us what test needs to be modified to ensure test runs are stable. AI can also handle test running on different resolutions and can optimize the wait time used in the test to wait for the page to load.

3. Dependencies on other modules

Writing a test for systems having dependencies on other modules is also a challenge. AI can help us to mock responses from a database or server. The AI can start recording server responses once we have written the test and have run them for a period of time. So the next time we run the test it will access the stored responses and will continue to run without any obstacles. This will speed up the process as the delay in response is eliminated and the server or physical database is no more needed.

4. Learning from production data

Real user data can be used to create an automated test and with the help of AI, we can observe and learn how the customer is using our product. We can identify common actions such as search option, using filters, login/logout, etc and compile them into reusable components. These components can be used for our test as well. Therefore, we have an actual test written by AI based on the real data along with the reusable components.

5. Easy execution of tests and speeding up the release

In automation testing, the time and effort it takes to write and execute a test is a major challenge due to the complexity of the test automation tools, app, and programing language used. To mitigate these problems AI-based tools are being used. The use of dynamic locators and reusable components has made it possible to write and execute a test in hours which earlier used to take a week.

Conclusion

The DevOps theory says test early, test often, but this puts a lot of responsibility on the testing team. Also, it’s not feasible for testing teams to spend time to do exploratory testing manually for each new release. AI-based tools can perform codeless automation testing which will save us time and resources and give the testers some space to breathe.

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