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Threshold-based prediction of schedule overrun in software projects

Conference Paper


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 e.ectiveness of our predictive models, achieving 78% precision, 56% recall, 65% F-measure, 84% Area Under the ROC Curve.

Publication Date


  • 2015

Citation


  • Choetkiertikul, M., Dam, H. Khanh. & Ghose, A. (2015). Threshold-based prediction of schedule overrun in software projects. In F. Kuo, S. Marshall, H. Shen, M. Stumptner & M. Ali Babar (Eds.), Proceedings of the 24th Australasian Software Engineering Conference (ASWEC 2015) (pp. 81-85). Piscataway, New Jersey, United States: IEEE.

Scopus Eid


  • 2-s2.0-84958794080

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5225

Start Page


  • 81

End Page


  • 85

Place Of Publication


  • Piscataway, New Jersey, United States

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 e.ectiveness of our predictive models, achieving 78% precision, 56% recall, 65% F-measure, 84% Area Under the ROC Curve.

Publication Date


  • 2015

Citation


  • Choetkiertikul, M., Dam, H. Khanh. & Ghose, A. (2015). Threshold-based prediction of schedule overrun in software projects. In F. Kuo, S. Marshall, H. Shen, M. Stumptner & M. Ali Babar (Eds.), Proceedings of the 24th Australasian Software Engineering Conference (ASWEC 2015) (pp. 81-85). Piscataway, New Jersey, United States: IEEE.

Scopus Eid


  • 2-s2.0-84958794080

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5225

Start Page


  • 81

End Page


  • 85

Place Of Publication


  • Piscataway, New Jersey, United States