Implementation and Comparison of Artificial Intelligence Techniques in Software Testing

AI is a prominent innovation of the current digital world. It powers most of the cutting-edge digital devices. It is a blend of unique technologies from machine learning (ML) to mixed reality (MR), and more. AI has significantly touched many sectors including healthcare, life sciences, manufacturing, retail, and agriculture. AI, as many expect, is going to be the future of the computing world. Artificial Intelligence has been a boon to the software industry as well. The software testing area has been highly touched by AI. Any applications that are built, need to be tested before providing it to the client. It becomes tedious if it’s a complex timebound application. Manual testing in those scenarios doesn’t seem to be a feasible solution. Although test automation has been incorporated in many companies, they aren’t efficient in all cases. AI, on the other hand, helps test the applications effectively. Machine learning and deep learning technologies of AI are playing a crucial role in training and inferring massive amounts of data resulting in faster testing of the application. In this paper, we discuss the implementation and comparison of machine learning and deep learning techniques in Software Testing.