To deliver error-free software, testing plays a crucial role. However, with the incorporation of Agile, DevOps, or DevSecOps development methodologies, testing has become a bane rather than a boon. According to GitlabDevSecOps 2021 Survey, the majority of enterprises consider testing as an area that is most likely to cause delays. If you’ve shifted IT infrastructure to the cloud and incorporated DevOps, or DevSecOps to deliver software faster, but you’re still sticking to legacy testing approaches, then the consequences of the testing bottleneck are severe such as deadlines slip, costs overrun, or projects failures. To stay ahead of the game, you need to transform testing. For this, you need to bring in AI-powered test automation.
Problems with legacy test automation approaches
Test Script Design: One of the key areas of test automation is to design a test automation script. If you’re using a code-based testing platform, designing automation scripts may not be easily performed by non-technical folks. The reason for this is that most code-based testing frameworks are designed with a programming mindset and testers are not programmers.
Test Script Maintenance: A test automation repository includes different test suites for functional testing, sanity testing, smoke testing, and regression testing to name just a few. Since most of the available enterprise applications such Oracle EBS, Oracle Fusion Cloud, Salesforce, etc feature dynamic elements, even minor UI changes like new screens, buttons, or user flows can lead to test failures or flaky/brittle tests.
Test Prioritization: Testers select smoke/regression tests cases based on their experience. Sometimes, they are moved by guesses rather than any logic. Statistics have suggested that often enterprises put their 100 percent effort just to achieve only 60%-65% of coverage. Under-testing and over-testing cannot serve your purpose as under-testing exposes your business to risks while over-testing consumes your time and budget.
Test Data: As test data is very important for testing, you need to ensure that enough and valid data is available during testing. However, if you’re testing enterprise applications like Oracle EBS, identifying qualiﬁed test data for processes like Procure to Pay and Order to Cash can be excruciating. Usually, in cases like Oracle Cloud testing, huge time and effort are required to create such exhaustive data sets.
How AI-powered test automation can help you?
Conventional test automation has moved a lot from a play and record engine to an autonomous test automation framework. Leveraging Ai-powered technologies like machine learning and natural language processing, testing frameworks can alleviate maintenance and authoring efforts. Let’s see how!
Autonomous Test Creation: Natural Language Processing (NLP) is a branch of AI that can address the challenges related to test script design. NLP enables Business Analysts, Manual Testers, QA Managers, or stakeholders to create test cases in a natural understandable language like English. This minimizes the test creation time and ensures adequate test coverage since anyone can contribute towards testing.
Self-healing: In highly dynamic enterprise applications like Oracle, test engineers struggle to maintain the test scripts each time a new update is rolled out. Since these apps don’t have fixed object properties i.e. Name, ID, Xpath, CSS, etc., a minor change can cause scripts to fail. Leveraging machine learning, this problem can be addressed. Ai-powered test automation frameworks automatically identify the change made to an element locator (ID) and self-healing capabilities make a without human intervention.
Test Recommendation: Leveraging risk-based test automation, QA teams can avoid executing the entire test suite for any small change in the enterprise apps. Ai-powered engines can identify the changes and impacted test cases to offer minimum tests to run for a given change made in the code.
Test Data: Leveraging “process mining” technology, configurations, and relevant data sets can be mined to provide test data to QA teams. Ai-based algorithms can be used to extract and modify production data that can be used to perform functional testing.
If you want to transform your enterprise and take advantage of new features, you need to transform your testing. Traditional testing approaches hold you back from “staying code current”. By incorporating Ai-powered test automation, you can keep up with market demands.