Empirical Analysis on Effectiveness of Source Code Metrics for Predicting Change-Proneness
Lov Kumar, Santanu Kumar Rath, Ashish Sureka · 2017
Change-prone classes or modules are defined as software components in the source code which are likely to change in the future. Change-proneness prediction are useful to the maintenance team as they can optimize and focus their testing resources on the modules which have a higher likelihood of change. The quality of change-proneness prediction model can be best assessed by the use of software metrics that are considered to design the prediction model. In this work, 62 software metrics with four metrics dimensions, including 7 size metrics, 18 cohesion metrics, 20 coupling metrics, and 17 inheritance metrics are considered to develop a model for predicting change-proneness modules. Since the performance of the change-proneness model depends on the source code metrics, they are used as input of the change-proneness model. We also considered five different types of feature selection techniques to remove irrelevant feature and select best set of features.