ABSTRACT
Risk identification is the first critical task of risk management for planning measures to deal with risks. While, software projects have a high risk of schedule overruns, current practices in risk management mostly rely on high level guidance and the subjective judgements of experts. In this paper, we propose a novel approach to support risk identification using historical data associated with a software project. Specifically, our approach identifies patterns of abnormal behaviours that caused project delays and uses this knowledge to develop an interpretable risk predictive model to predict whether current software tasks (in the form of issues) will cause a schedule overrun. The abnormal behaviour identification is based on a set of configurable threshold-based risk factors. Our approach aims to provide not only predictive models, but also an interpretable outcome that can be inferred as the patterns of the combinations between risk factors. The evaluation results from two case studies (Moodle and Duraspace) demonstrate the effectiveness of our predictive models, achieving 78% precision, 56% recall, 65% F-measure, 84% Area Under the ROC Curve.
- B. W. Boehm. Software risk management: principles and practices. Software, IEEE, 8(1):32--41, 1991. Google ScholarDigital Library
- M. J. Carr and S. L. Konda. Taxonomy-Based Risk Identification. Technical Report June, Software Engineering Institute, Carnegie Mellon University, 1993.Google ScholarCross Ref
- M. Choetkiertikul, H. K. Dam, T. Tran, and A. Ghose. Characterization and prediction of issue-related risks in software projects. In Proceedings of 12th Working Conference on Mining Software Repositories (MSR-2015), page To Appear, 2015. Google ScholarDigital Library
- K. de Bakker, A. Boonstra, and H. Wortmann. Does risk management contribute to IT project success? A meta-analysis of empirical evidence. International Journal of Project Management, 28(5):493--503, July 2010.Google ScholarCross Ref
- J. L. Eveleens and C. Verhoef. The rise and fall of the Chaos report figures. IEEE Software, 27(1):30--36, 2010. Google ScholarDigital Library
- E. Giger, M. Pinzger, and H. Gall. Predicting the fix time of bugs. In Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering - RSSE '10, pages 52--56. ACM Press, May 2010. Google ScholarDigital Library
- P. Hooimeijer and W. Weimer. Modeling bug report quality. In Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering - ASE '07, page 34. ACM Press, Nov. 2007. Google ScholarDigital Library
- L. Marks, Y. Zou, and A. E. Hassan. Studying the fix-time for bugs in large open source projects. In Proceedings of the 7th International Conference on Predictive Models in Software Engineering - Promise '11, pages 1--8, New York, New York, USA, Sept. 2011. ACM Press. Google ScholarDigital Library
- B. Michael, S. Blumberg, and J. Laartz. Delivering large-scale IT projects on time, on budget, and on value. In McKinsey Quarterly, 2012.Google Scholar
- A. Pika, W. M. van der Aalst, C. J. Fidge, A. H. ter Hofstede, M. T. Wynn, and W. V. D. Aalst. Profiling event logs to configure risk indicators for process delays. Advanced Information Systems Engineering (CAISE 2013), pages 465--481, July 2013. Google ScholarDigital Library
- A. A. Porter, H. P. Siy, and L. G. Votta. Understanding the effects of developer activities on inspection interval. In Proceedings of the 19th international conference on Software engineering - ICSE '97, pages 128--138. ACM Press, May 1997. Google ScholarDigital Library
- J. R. Quinlan. C4. 5: programs for machine learning. Elsevier, 2014.Google ScholarDigital Library
- P. J. Rousseeuw and A. M. Leroy. Robust regression and outlier detection, volume 589. John Wiley & Sons, 2005.Google Scholar
- P. Runeson, M. Alexandersson, and O. Nyholm. Detection of Duplicate Defect Reports Using Natural Language Processing. In 29th International Conference on Software Engineering (ICSE'07), pages 499--510. IEEE, May 2007. Google ScholarDigital Library
- E. Shihab, A. Ihara, Y. Kamei, W. M. Ibrahim, M. Ohira, B. Adams, A. E. Hassan, and K.-i. Matsumoto. Studying re-opened bugs in open source software. Empirical Software Engineering, 18(5):1005--1042, Sept. 2012.Google ScholarCross Ref
- L. Tichy and T. Bascom. The business end of IT project failure. Mortgage Banking, 68(6):28, 2008.Google Scholar
- C. Weiss, R. Premraj, T. Zimmermann, and A. Zeller. How Long Will It Take to Fix This Bug? In Proceedings - ICSE 2007 Workshops: Fourth International Workshop on Mining Software Repositories, MSR 2007. IEEE, May 2007. Google ScholarDigital Library
- T. Zimmermann, N. Nagappan, P. J. Guo, and B. Murphy. Characterizing and predicting which bugs get reopened. In 34th International Conference on Software Engineering (ICSE), 2012, pages 1074--1083. IEEE Press, June 2012. Google ScholarDigital Library
Index Terms
- Threshold-based prediction of schedule overrun in software projects
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