Many industries are being revolutionized through the use of artificial intelligence and machine learning to automate monotonous jobs. For instance, there has been an increasing application of AI in software testing. Test automation has been on use for years towards quickening of testing, but integration of the latest AI technology makes test automation even strong and smart. This blog post outlines five notable reasons why adopting AI in Test automation can benefit organizations.

5 Reasons to Use AI in Test Automation

Faster test execution

Test automation allows running test cases which are impossible to execute manually within such short time frames and number of runs. Furthermore, AI goes beyond this by learning from previous test runs and optimizing test execution. Compared with other conventional test automation codes, it is possible to run tests in several environments at once using AI much quicker. This effectively cuts test cycle times allowing QA staff concentrate on more strategic areas of work. Similarly, since AI is aware of precedent test results, it skips executing unnecessary test cases that would further waste the time.

Self-healing test scripts

Test automation scripts that are developed by hand end up in continuous updating over time, since new versions and enhancements of application occur. Whenever there are changes in codes and the user interface (UI), test cases break down often. Test scripts can auto heal and auto update themselves with AI. AI systems are capable of analyzing screen shots, page elements and application code and updating test steps, locators and validations which might fail. The self-healing characteristic cuts down on maintenance time and ensures that automation scripts are always updated. Development does not stop anymore because of broken tests cases.

Detecting regressions early

Automated testing assisted by artificial intelligence reduces the cost of regression tests by predicting likely buggy components in a latest release based on their prior behavior. It detects tests that have high chances of failure due to code modification and past results. Through running such high-risk test cases first allows for the development of regression alerts that help detect problems earlier thereby giving room for a timely fixing of bugs. Regressions are identified at an early stage which leads to less reworks and no customer impact.

Test case prioritization and optimization

Prioritization and optimization of a big test collection requires much effort and time spent manually. By using methods such as machine learning and deep learning process for analyzing test cases, requirements, code modification and prior execution record, the AI improves optimization of the test suite. With this, test cases can be appropriately prioritized by focusing on those in high-risk zones and minimizing low-priority cases. In addition, AI can spot and delete redundant test cases which are adding no value. By constantly reevaluating test suites through AI, such optimization results in optimal coverage with minimal labor cost. Quality of software is obtained via an efficient use of resources.

Intelligent test case design

Test case development is a very knowledge and an experience-based process. AI can assist in the automation of the test design process through auto-generation of fresh test cases not previously covered. This looks into software requirement specifications, business requirements, edge cases and comes up with additional scenarios that can be covered. After evaluating test outcomes, AI also proposes amendments to existing testing procedures. Use of AI assistance in designing such tests enhances the scope, increases quality and propels testing beyond what humans can achieve on their own.

Conclusion

The key advantages from using AI in testing automation platform include speed, maintenance, optimization, intelligence, and helping companies produce better software quicker. Opkey is an AI-powered test automation platform that streamlines the testing process. Its change impact assessment tool identifies impacted test cases using AI and recommends high priority tests based on risk. This allows QA teams to focus only on relevant testing, saving time and improving coverage. Opkey also autonomously heals broken test scripts without human effort, taking the pain out of maintenance. It effortlessly keeps tests running smoothly. If you want to learn more about how Opkey can optimize your testing, book a quick demo to see it in action.