Examining the Effectiveness of Machine Learning Algorithms for Prediction of Change Prone Classes

Managing change in the early stages of a software development life cycle is an effective strategy for developing a good quality software at low costs. In order to manage change, we use software quality models which can efficiently predict change prone classes and hence guide developers in appropriate distribution of limited resources. This study examines the effectiveness of ten machine learning algorithms for developing such software quality models on three object-oriented software data sets. We also compare the performance of machine learning algorithms with the widely used logistic regression technique and statistically rank various algorithms with the help of Friedman test.