Predicting change using software metrics: A review

Software change prediction deals with identifying the classes that are prone to changes during the early phases of software development life cycle. Prediction of change prone classes leads to higher quality, maintainable software with low cost. This study reports a systematic review of change prediction studies published in journals and conference proceedings. This review will help researchers and practitioners to examine the previous studies from different viewpoints: metrics, data analysis techniques, datasets, and experimental results perspectives. Besides this, the research questions formulated in the review allow us to identify gaps in the current technology. The key findings of the review are: (i) less use of method level metrics, machine learning methods and commercial datasets; (ii) inappropriate use of performance measures and statistical tests; (iii) lack of use of feature reduction techniques; (iv) lack of risk indicators used for identifying change prone classes and (v) inappropriate use of validation methods.