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Characterization and prediction of issue-related risks in software projects

Conference Paper


Abstract


  • Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing 'risky' software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. The extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48% - 81% precision, 23% - 90% recall, 29% - 71% F-measure, and 70% - 92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39 - 0.75 for Macro-averaged Mean Cost-Error and 0.7 - 1.2 for Macro-averaged Mean Absolute Error.

UOW Authors


  •   Choetkiertikul, Morakot (external author)
  •   Dam, Hoa
  •   Tran, Truyen (external author)
  •   Ghose, Aditya

Publication Date


  • 2015

Citation


  • Choetkiertikul, M., Dam, H. Khanh., Tran, T. & Ghose, A. (2015). Characterization and prediction of issue-related risks in software projects. IEEE International Working Conference on Mining Software Repositories (pp. 280-291). United States: IEEE.

Scopus Eid


  • 2-s2.0-84957075605

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5026

Has Global Citation Frequency


Start Page


  • 280

End Page


  • 291

Place Of Publication


  • United States

Abstract


  • Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing 'risky' software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. The extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48% - 81% precision, 23% - 90% recall, 29% - 71% F-measure, and 70% - 92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39 - 0.75 for Macro-averaged Mean Cost-Error and 0.7 - 1.2 for Macro-averaged Mean Absolute Error.

UOW Authors


  •   Choetkiertikul, Morakot (external author)
  •   Dam, Hoa
  •   Tran, Truyen (external author)
  •   Ghose, Aditya

Publication Date


  • 2015

Citation


  • Choetkiertikul, M., Dam, H. Khanh., Tran, T. & Ghose, A. (2015). Characterization and prediction of issue-related risks in software projects. IEEE International Working Conference on Mining Software Repositories (pp. 280-291). United States: IEEE.

Scopus Eid


  • 2-s2.0-84957075605

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5026

Has Global Citation Frequency


Start Page


  • 280

End Page


  • 291

Place Of Publication


  • United States