Transformation to Quality Engineering through AI with Automation using Machine Learning

Abstract

The application of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized quality engineering by making intelligent automation, predictive analytics and adaptive software testing possible. However, traditional quality engineering techniques can often prove difficult to implement, scale, integrate and control defects in modern software environments. In this study, we present a novel quality engineering transformation framework through the integration of machine learning and automated quality assurance processes to increase software reliability, reduce test time and improve prediction capabilities for software defects. The AI-enabled framework presented in the research uses intelligent analytics to perform adaptive risk assessment, automated optimization of test cases and quality monitoring at all stages of software engineering. Furthermore, the framework makes use of the behavioral learning model to optimize testing techniques based on previous project experiences and knowledge. The benefits of the experiment in comparison with traditional quality engineering techniques can be easily illustrated with the help of experimental findings; they include increased efficiency and accuracy in detecting software defects, resource optimization, faster test processing and improved overall software quality.