Learning Feature Representations from Change Dependency Graphs for Defect Prediction

Given the heterogeneity of the data that can be extracted from the software development process, defect prediction techniques have focused on associating different sources of data with the introduction of faulty code, usually relying on handcrafted features. While these efforts have generated considerable progress over the years, little attention has been given to the fact that the performance of any predictive model depends heavily on the representation of the data used, and that different representations can lead to different results. We consider this a relevant problem, as it could be affecting directly the efforts towards generating safer software systems. Therefore, we propose to study the impact of the representation of the data in defect prediction models. To this end, we focus on the use of developer activity data, from which we structure dependency graphs. Then, instead of manually generating features, such as network metrics, we propose two models inspired by recent advances in representation learning which are able to automatically generate feature representations from graph data. These new representations are compared against manually crafted features for defect prediction in real world software projects. Our results show that automatically learned features are competitive, reaching increments in prediction performance up to 13%.