An Empirical Study to Evaluate the Relationship of Object-Oriented Metrics and Change Proneness

Software maintenance deals with changes or modifications which software goes through. Change prediction models help in identification of classes/modules which are prone to change in future releases of a software product. As change prone classes are probable sources of defects and modifications, they represent the weak areas of a product. Thus, change prediction models would aid software developers in delivering an effective quality software product by allocating more resources to change prone classes/modules as they need greater attention and resources for verification and meticulous testing. This would reduce the probability of defects in future releases and would yield a better quality product and satisfied customers. This study deals with the identification of change prone classes in an Object-Oriented (OO) software in order to evaluate whether a relationship exists between OO metrics and change proneness attribute of a class. The study also compares the effectiveness of two sets of methods for change prediction tasks i.e. the traditional statistical methods (logistic regression) and the recently widely used machine learning methods like Bagging, Multi-layer perceptron etc.