Predicting software change-proneness with code smells and class imbalance learning

Abstract

The objective of this paper is to study the relationship between different types of object-oriented software metrics, code smells and actual changes in software code that occur during maintenance period. It is hypothesized that code smells are indicators of maintenance problems. To understand the relationship between code smells and maintenance problems, we extract code smells in a Java based mobile application called MOBAC. Four versions of MOBAC are studied. Machine learning techniques are applied to predict software change-proneness with code smells as predictor variables. The results of this paper indicate that codes smells are more accurate predictors of change-proneness than static code metrics for all machine learning methods. However, class imbalance techniques did not outperform class balance machine learning techniques in change-proneness prediction. The results of this paper are based on accuracy measures such as F-measure and area under ROC curve.