Cost Effectiveness of Software Defect Prediction in an Industrial Project

Abstract

Abstract Software defect prediction is a promising approach aiming to increase software quality and, as a result, development pace. Unfortunately, the cost effectiveness of software defect prediction in industrial settings is not eagerly shared by the pioneering companies. In particular, this is the first attempt to investigate the cost effectiveness of using the DePress open source software measurement framework (jointly developed by Wroclaw University of Science and Technology, and Capgemini software development company) for defect prediction in commercial software projects. We explore whether defect prediction can positively impact an industrial software development project by generating profits. To meet this goal, we conducted a defect prediction and simulated potential quality assurance costs based on the best possible prediction results when using a default, non-tweaked DePress configuration, as well as the proposed Quality Assurance (QA) strategy. Results of our investigation are optimistic: we estimated that quality assurance costs can be reduced by almost 30% when the proposed approach will be used, while estimated DePress usage Return on Investment (ROI) is fully 73 (7300%), and Benefits Cost Ratio (BCR) is 74. Such promising results, being the outcome of the presented research, have caused the acceptance of continued usage of the DePress-based software defect prediction for actual industrial projects run by Volvo Group.