Leveraging AI In Custom Software Testing
Quality assurance plays a critical role in custom software testing. It is a litmus test of the software’s functionalities, defining whether it is ready to be released or not. And as AI has found its application in the realm of custom software development too, software testing and quality assurance has also seen a major transformation. Let’s take a closer look at how artificial intelligence is reshaping quality assurance in software testing and where it is headed altogether.
Applications Of AI In Custom Software Testing
Use of machine learning
Machine learning facilitates proactive QA by assisting in predicting defects through analyzing historical data patterns. By leveraging machine learning in software testing, QA teams can identify and fix bugs before the software is released to the end-user.
Automated test generation
Automated test generation is the process of automatically identifying, creating and executing test cases for custom software solutions. It utilizes artificial intelligence and machine learning techniques with minimal human intervention to generate test cases. Automated test generation helps identify software defects, individual feature defects, and any other issues that may affect the end-user’s experience. It allows software teams to fix bugs and defects before releasing the software.
NLP-based test automation
Test automation has been a game-changer in how software testing is carried out. If back in the days (or even today in some software companies) tests were written by humans, which often resulted in poor test coverage or inability to conduct regressive testing with 100% efficiency, today, automated testing is marked by better test coverage and test accuracy. Natural language processing has made test automation even more advanced. In NLP-based test automation, a tester inputs pre-defined requirements, which are then used to generate a test case.
AI-powered regression testing
Regression testing is a software testing practice where the software is tested after introducing a code update to ensure the new update has not adversely affected the existing code. AI-powered regression testing brings a major improvement to the regression testing by increasing the speed and quality of the testing process and thus reducing custom software solutions’ time-to-market.
Conclusion
AI has dramatically changed the existing software testing paradigms by reducing the time and efforts it takes to develop test cases. It has made software quality assurance more efficient, resulting in better quality custom software solutions that meet the needs of end-users. Custom software development companies should seriously consider implementing AI technology into their software testing routines to improve the quality of custom software solutions they deliver to customers.
