Characterization and prediction of issue-related risks in software projects
Contribuinte(s) |
[Unknown] |
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Data(s) |
01/01/2015
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30073757/tran-characterizationand-2015.pdf http://dro.deakin.edu.au/eserv/DU:30073757/tran-characterizationand-evid-2015.pdf http://dro.deakin.edu.au/eserv/DU:30073757/tran-characterizationand-post-2015.pdf http://doi.org/10.1109/MSR.2015.33 |
Direitos |
2015, IEEE |
Tipo |
Conference Paper |