5 ways to Leverage AI in Test Automation

Even in today’s digital world, where the software development life cycle needs to be quick and scalable, quality is paramount. But it is challenging to ensure quality and speed to keep up with continuous integration and continuous delivery (CI/CD), which are of the essence in Agile and DevOps practises. In addition, achieving quality is not inexpensive either.

Organizations have now started leveraging artificial intelligence (AI) to improve and increase Test automation. Smarter and cost-effective testing is another key benefit of using the technology, which not only helps identify bugs faster but also identifies what needs testing and where bugs are likely to be found.

How AI Enhances Test Automation

Test automation has largely been rule-based but AI goes beyond the scope of rule-based automation with AI and machine learning algorithms training computers with large datasets.

Below are five examples (or ways) artificial intelligence enhances Test automation.

Run More Relevant Automated Tests

If you were to make minor changes to your code and need to trace them, is it necessary to run the entire suite of automated test cases? In the case of traditional Test automation, answering this question is virtually impossible.

But the advanced analytics capabilities of AI/ML algorithms make it possible to identify the necessary test cases that need to be run to test a change to the code.

The key difference?

It enhances the capability to run tests that matter while also helping to analyze test coverage and improve it.

Improve the Reliability of Automated Tests

It is possible that some tests fail because a developer made a change to the application (such as renaming a field ID, for example).

Tools, however, adapt to these changes automatically, which improves the maintainability and reliability of the automated tests.

Once a tool learns and observes the changes to the application, it decides automatically during runtime which locators should be used to detect an element.

Should the application have more changes, the script adjusts itself accordingly.

Test Case Creation

What separates AI-based Test automation from rule-based is the algorithm’s ability to read and learn about an application. As the tool craws the application, it gathers data about features through screenshots, by measuring load times, downloading a page’s HTML and more.

From the learnings, algorithms create datasets with observations about the application, even including how its features must adapt to different conditions.

This helps in the automatic creation of test cases to go with recording the projected results. This way of test case creation is superior in comparison with rule-based Test automation and what it can achieve.

Testing API

A significant part of testing today is backend related. This comes down to testing APIs, the acronym for application programming interface, which is an integral part of application development.

AI minimizes the complexity of API testing by converting manual user interface (UI) tests into automated API tests. For example, testers aren’t required to find the right service definition, understand data payload, run tests repeatedly to identify the relationship between requests and responses, and so on.

This in turn helps organizations build a scalable and manageable API test automation strategy, while also minimizing the technical skills required to adopt API testing.

AI-Enabled Visual Validation

The main aim of image-based visual validation is to make sure that the visual elements of the user interface display correctly at the application’s front-end. That means, the different UI elements must appear in the assigned color, size, shape, position, et cetera. They also shouldn’t hide or overlap other elements.

Rule-based test automation is not helpful in automating this type of testing. Even manual testing does not help detect the discrepancies.

Machine learning-based visual validation tools detect discrepancies that could likely be missed by human testers. Testers can create a machine learning test to identify all the visual bugs in the software and verify the visual suitability of the application.

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AI-powered Test automation tools, because of their ability to learn and adapt, help implement automation at a level which is difficult to realize with rule-based automation. Whether you are a tester or an IT leader working with an enterprise, you should embrace the AI-driven innovation in the field of Test automation. That means, it is not only essential to know automation but also be able to comprehend complex data structures and algorithms to help create testing models.

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