Learning Graph Representations for Defect Prediction
Pablo Loyola, Yutaka Matsuo · 2017
We propose to study the impact of the representation of the data in defect prediction models. For this study, 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 a model inspired in recent advances in Representation Learning which are able to automatically learn representations from graph data. These new representations are compared against manually crafted features for defect prediction in real world software projects.