A Framework for Automated Software Testing using Machine Learning and Artificial Intelligence

In order to produce high-quality applications while decreasing human labor and speeding up application development, automatic software testing is a crucial development methodology. This paper presents a novel framework that uses machine learning and artificial intelligence to improve automated software testing through intelligent solutions. Through machine learning techniques that perform test case selection and bug prediction with programming code base change monitoring, the framework achieves its optimization. Testing processes benefit from automated learning features that boost testing performance in real-time and artificial intelligence-based models that enhance error detection capabilities. When findings from contemporary methodologies surpass those from traditional testing methods, there are improvements in test coverage as well as fault detection capabilities and overall efficiency. This framework proposes a potential approach to the testing of AI and machine learning that takes into account adaption constraints as well as test scalability issues and automation dependability.