How to Adapt Machine Learning into Software Testing

摘要

Software testing cycles have several difficulties, such as coverage of a dense scope in a limited time, due to dynamic product development approaches. Researchers try to use new techniques to overcome these difficulties. This paper presents the utilization of Machine Learning (ML) in software testing stages with its effects and outcomes. Practical applications and advantages are analyzed. The main goal is to make insights about what can be done in different stages of software testing by employing ML and discuss benefits and risks.