Smart learning: A search-based approach to rank change and defect prone classes

Research has yielded approaches for predicting future changes and defects in software artifacts, based on historical information, helping developers in effectively allocating their (limited) resources. Developers are unlikely able to focus on all predicted software artifacts, hence the ordering of predictions is important for choosing the right artifacts to concentrate on. We propose using a Genetic Algorithm (GA) for tailoring prediction models to prioritize classes with more changes/defects. We evaluate the approach on two models, regression tree and linear regression, predicting changes/defects between multiple releases of eight open source projects. Our results show that regression models calibrated by GA significantly outperform their traditional counterparts, improving the ranking of classes with more changes/defects by up to 48%. In many cases the top 10% of predicted classes can contain up to twice as many changes or defects. Keywords—code change, defect prediction, genetic algorithm