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Characterization and prediction of issue-related risks in software projects

Choetkiertikul, Morakot, Dam, Hoa Khanh, Tran, Truyen and Ghose, Aditya 2015, Characterization and prediction of issue-related risks in software projects, in MSR 2015 : Proceedings of the 12th Working Conference on Mining Software Repositories, IEEE, Piscataway, N.J., pp. 280-291, doi: 10.1109/MSR.2015.33.

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Title Characterization and prediction of issue-related risks in software projects
Author(s) Choetkiertikul, Morakot
Dam, Hoa Khanh
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Ghose, Aditya
Conference name Mining Software Repositories. Working Conference (12th : 2015 : Florence, Italy)
Conference location Florence, Italy
Conference dates 16-17 May. 2015
Title of proceedings MSR 2015 : Proceedings of the 12th Working Conference on Mining Software Repositories
Editor(s) [Unknown]
Publication date 2015
Start page 280
End page 291
Total pages 12
Publisher IEEE
Place of publication Piscataway, N.J.
Summary 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.
Language eng
DOI 10.1109/MSR.2015.33
Field of Research 080303 Computer System Security
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30073757

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.