What is AI automation testing?
The use of Artificial Intelligence (AI) in software testing is the latest trend, but, is gradually turning out to be rewarding. AI in test automation can actually scale up the performance of testing activities. AI might be able to help resolve the test automation challenges and thus improvise the process of automation testing. AI and test automation can be integrated to take the testing activities a notch higher, provided the concerned team knows what exactly should be the strategic implementation to make it a success. In this article, you will know about AI automation testing.
Following are four factors that depict the application of AI in automation testing:
1. Unit tests need to be updated and created: Developers put in a lot of efforts to maintain unit tests. AI-based products can prove to be useful for automated unit test creation, especially for those organizations that are planning to introduce unit tests in the later stages of the product life cycle.
AI-based automated unit tests are way better than template-based automated unit tests that use static or dynamic analysis. AI-based unit tests can be generated at a rapid pace, which is, in turn, beneficial for large existing codebase. Tests need to be modified by developers and then the unit regression suite can be quickly setup.
2. Automated UI testing: There is one specific domain (among the many) where AI is gaining dominance and that is AI-based UI testing. In this testing method, DOM and the related code are parsed by test automation tools to ascertain object properties. Image recognition techniques can be used so that the application can be navigated to visually verify UI objects and elements in order to create UI tests.
Exploratory testing is used by AI test systems to find variations or bugs in the UI application and generate screenshots, which can be later verified by a QA engineer. Similarly, the visual features of the System Under Test such as color, size and layout can be verified. Code coverage can be increased through automated UI testing. The test suite will not fail if there are minor deviations in the UI. It can be handled by product AI models.
3. Integrating AI in API testing: API needs to be understood and then tests can be setup for a multitude of scenarios to ensure breadth and depth of coverage. API test automation tools such as SoapUI and Tricentis record traffic and API activities to create and analyse tests. However, updating and modifying tests require testers to delve deep into REST calls and parameters, so that the API test suite can be updated.
AI-based API automation testing tools attempt to ease this problem by identifying patterns and examining traffic and connections between API calls, thereby grouping them by scenario. Existing tests are used by tools to learn the relationships between APIs so that changes in APIs can be properly done. Upon that, existing tests can be updated and new scenario-based tests can be created.
4. Generating test data through AI platform: Another significant area for AI models is test data generation. Data sets like age and weight information and personal profile photographs can be easily generated by machine learning. This is made possible through trained machine learning models by using the existing production datasets.
The test data that is generated is almost similar to production data which in turn can be highly useful in software testing. Data is generated through a machine learning model that is called a Generative Adversarial Network (GAN).
Conclusion: If you are looking for more in-depth strategic information on AI automation testing that may be of worth for your specific project needs, then do get connected with a premium software testing services company that will provide you the best market-relevant solution.
