A multi-source machine learning approach to predict defect prone components

Software code life cycle is characterized by continuous changes requiring a great effort to perform the testing of all the components involved in the changes. Given the limited number of resources, the identification of the defect proneness of the software components becomes a critical issue allowing to improve the resources allocation and distributions. In the last years several approaches to evaluating the defect proneness of software components are proposed: these approaches exploit products metrics (like the Chidamber and Kemerer metrics suite) or process metrics (measuring specific aspect of the development process). In this paper, a multi-source machine learning approach based on a selection of both products and process metrics to predict defect proneness is proposed. With respect to the existing approaches, the proposed classifier allows predicting the defect proneness basing on the evolution of these features across the project development. The approach is tested on a real dataset composed of two well-known open source software systems on a total of 183 releases. The obtained results show that the proposed features have effective defect proneness prediction ability.