Towards Generalizing Defect Prediction Models

Software quality is vital to the success of a software project. Fixing defects is the major activity to continuously improve software quality. Given that a real development team usually exhibits limited resources and tight schedules, it is important to prioritize testing activities and optimize development resources. Predicting defective entities (e.g., files or classes) ahead helps achieve such a goal. Defect prediction has attracted considerable attention from both academia and industry in the last decade. A typical defect prediction model is built upon software metrics and labelled defect data that are collected from the historical data of a software project. A defect prediction model can be applied within the same project (within-project defect prediction) or on other projects (cross-project defect prediction). However, due to the diversity in development processes, a defect prediction model is often not transferable and requires to be rebuilt when the target project changes. As it consumes additional effort to build and maintain a defect prediction model for a particular project, it is of significant interest to generalize a defect prediction model. A generalized defect prediction model relieves the need to rebuild a defect prediction model for each target project. Moreover, it helps reveal a general relationship between software metrics and defect data. In this thesis, we analyze the feasibility of generalizing defect prediction models. First, we analyze how the distribution of the values of software metrics varies across projects of different context factors (e.g., programming language and system size). We observe that such distributions do vary across projects, but can also be similar across projects of different context factors. Second, we investigate the impact that the pre-processing steps (in particular, transformation and aggregation of software metrics) have on the performance of defect prediction models. We find that the pre-processing steps impact the performance of defect prediction models, and therefore need to be considered towards generalizing defect prediction models. Finally, we propose two approaches for generalizing defect prediction models with supervised (requiring the training data) and unsupervised (without the training data) methods, respectively. Our results show that both approaches are feasible to generalize defect prediction models.