Characterization and prediction of issue-related risks in software projects
© 2015 IEEE. 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 nove...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/16791 |
| _version_ | 1848749278168612864 |
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| author | Choetkiertikul, M. Dam, H. Tran, The Truyen Ghose, A. |
| author_facet | Choetkiertikul, M. Dam, H. Tran, The Truyen Ghose, A. |
| author_sort | Choetkiertikul, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015 IEEE. 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. |
| first_indexed | 2025-11-14T07:18:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-16791 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:18:24Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-167912018-03-29T09:06:20Z Characterization and prediction of issue-related risks in software projects Choetkiertikul, M. Dam, H. Tran, The Truyen Ghose, A. © 2015 IEEE. 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. 2015 Conference Paper http://hdl.handle.net/20.500.11937/16791 10.1109/MSR.2015.33 restricted |
| spellingShingle | Choetkiertikul, M. Dam, H. Tran, The Truyen Ghose, A. Characterization and prediction of issue-related risks in software projects |
| title | Characterization and prediction of issue-related risks in software projects |
| title_full | Characterization and prediction of issue-related risks in software projects |
| title_fullStr | Characterization and prediction of issue-related risks in software projects |
| title_full_unstemmed | Characterization and prediction of issue-related risks in software projects |
| title_short | Characterization and prediction of issue-related risks in software projects |
| title_sort | characterization and prediction of issue-related risks in software projects |
| url | http://hdl.handle.net/20.500.11937/16791 |