<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Faculty of Computer Science and Management

Wroclaw University of Science and Technology

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Cost Effectiveness of Software Defect Prediction in an Industrial Project"}

Software defect prediction is a promising approach aiming to increase software quality and, as a result, development pace. Unfortunately, the cost...

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Online veröffentlicht: 16. Feb. 2018

Seitenbereich: 7 - 35

Eingereicht: 12. Juni 2017

Akzeptiert: 09. Jan. 2018

DOI: https://doi.org/10.1515/fcds-2018-0002

Schlüsselwörter
software defect prediction, industrial application, quality assurance, quality assurance cost

© 2018 Jaroslaw Hryszko et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Online veröffentlicht: 16. Feb. 2018

Seitenbereich: 7 - 35

Eingereicht: 12. Juni 2017

Akzeptiert: 09. Jan. 2018

DOI: https://doi.org/10.1515/fcds-2018-0002

Schlüsselwörtersoftware defect prediction, industrial application, quality assurance, quality assurance cost

© 2018 Jaroslaw Hryszko et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
software defect prediction, industrial application, quality assurance, quality assurance cost