24
Sep
2021

Four Applications Of AI in Test Automation

In the mobile app testing domain, the importance of automation is well known. The shift to test automation has enabled the testers to make a considerable leap and efficiency in-app testing. Automation is becoming a significant part of agile methodology. The process of building, testing, and validating application and its components are being performed at a much lesser time and cost, hence shrinking the app’s time-to-market. Test automation supports continuous tests, takes care of regression tests, and generates test reports which help the release the high-performing apps.

Tools are critical drivers of a mobile test automation project. Test automation tools enable the stakeholders to automate their app testing needs and minimize human intervention. It helps to achieve more incredible speed and accuracy in mobile app testing. The domain of test automation is advancing and is leveraging the sophistication of automation testing tools and approaches. Artificial intelligence or AI is bringing seismic shifts in test automation. In-app testing, AI combines cognitive automation, reasoning, machine learning, natural language processing, and analytics. The massive need to test algorithms and perform continuous testing has accelerated the adoption of AI in test automation.

Test automation requires continuous monitoring and maintenance. The recent advancement in AI capabilities within software testing is making test automation more feasible and practical. AI technology can use extensive data to train models and make predictions or generate some desired output. With the introduction of AI in-app testing, the stakeholders make the entire process more data-driven instead of relying on assumptions. Hence, the test automation strategy is becoming more streamlined, allowing testers to align the testing approach with the business objectives. Here are few essential use-cases of AI in test automation.

Automating Visual Validation

Machine learning is a pattern recognition technique. ML facilitates image-based testing, and the ML-based visual validation tools can find various user interface anomalies which human testers may miss. UI testing is an integral part of the app testing suite and helps verify that the app’s client-side rendering is optimized. It helps verify that the font, shape, size, and position of the various UI elements are true to its blueprint. Visual testing is a part of QA tests to verify that the UI appears correctly on the end user’s handsets. These tests are a perfect fit for AI testing. It supports the automatic detection of visual bugs to ensure the visual correction of the app so that the UI elements do not hide or overlap.

Automatically Write Test Cases

One of the most prominent use-cases of AI in software testing is the automation of writing test cases. AI/ML can learn various aspects of the application under tests. It can automatically crawl for collecting valuable data such as screenshots, HTML pages, and page loading time. Once it contains a sufficient amount of data, it uses the data to train the ML model and learn about the expected patterns of the app. ML can automatically write tests for the application via this spidering method. When it runs, it compares the app’s current state to the known or saved patterns. The ML model automatically highlights it as a potential issue or insight if it recognizes any error, visual difference, or other problems. It performs the heavy lifting, and the human tester needs to verify the flagged issue for further testing.

Improve Testing Reliability

Small changes in applications tend to disturb the entire testing workflow. With AI, however, testers can significantly improve testing reliability even if your app keeps changing. It enables the test script to adjust itself automatically. It can correct the code, enhancing its dependency and maintainability. Hence, you will not have to change the test scripts for automation testing whenever a small change occurs in your app. AI can learn about the changes and understand the relationship between the code changes. It can evaluate code changes and fix several existing tests. As it can adjust scripts automatically, it reduces the friction in test automation.

Assist In API Testing

Automating API testing is a challenging task. The testers need to understand the API and set up multiple test scenarios for more excellent test coverage. The API test automation tools can record API traffic and activities for creating and analyzing tests. But, it isn’t easy to update and modify tests, and it requires the tester to delve deeper into the complexities of the API. AI-based API automation testing tools can help to solve this problem. It allows the mitigation of the issues as it can analyze traffic and identify patterns among API calls, and based on the scenario, it can group it appropriately. The pattern-learning technique allows the AI-based tools to update the tests or create new scenario-based tests automatically.

To Conclude:

AI-based automation testing is advancing and is yet to reach a mass-scale adoption. However, the benefits which it leverages in test automation are contributing to its popularity. AI is bearing a significant impact on testing tools and test automation strategies. While it is still on its way to maturity, testers must try to keep pace with it to facilitate its adoption in their Agile and DevOps practices when the time is right.

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