Assessment of the Software Defect Prediction Cost Effectiveness in an Industrial Project

Abstract

Software defect prediction is a promising, new approach to increase both, software quality and development pace. Unfortunately, the cost effectiveness of software defect prediction in industrial settings is not eagerly shared by the pioneering companies. In particular, the cost effectiveness of using the DePress open source software measurement framework, developed by Wroclaw University of Science and Technology, and Capgemini software development company, for defect prediction in commercial software development projects have not been previously investigated. Thus, in this paper, 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 prediction result, as well as the proposed Quality Assurance (QA) strategy. Results of our investigation were optimistic: we estimated that quality assurance costs can be reduced by almost 30 % when proposed approach will be used, while estimated DePress tool usage Return on Investment (ROI) is fully 73 (7300 %), and Benefits Cost Ratio (BCR) is 74. Such promising results have caused the acceptance of continued usage of the DePress-based software defect prediction for actual industrial projects run by Volvo Group.