![]() The research community is still facing a number of challenges for building methods and many research opportunities exist. The selected defect prediction papers are summarised to four aspects: machine learning-based prediction algorithms, manipulating the data, effort-aware prediction and empirical studies. The authors survey almost 70 representative defect prediction papers in recent years (January 2014–April 2017), most of which are published in the prominent software engineering journals and top conferences. The goal of this study is to comprehensively review, analyse and discuss the state-of-the-art of defect prediction. In recent years, especially for recent 3 years, many new defect prediction studies have been proposed. It aims to predict defect-prone software modules before defects are discovered, therefore it can be used to better prioritise software quality assurance effort. Software defect prediction is one of the most popular research topics in software engineering. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology. ![]()
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