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Characterization and prediction of issue-related risks in software projects

conference contribution
posted on 2015-01-01, 00:00 authored by M Choetkiertikul, Truyen TranTruyen Tran, H Dam, A Ghose
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 Macroaveraged Mean Cost-Error and and 0.7–1.2 for Macro-averaged Mean Absolute Error.

History

Pagination

280-291

Location

Florence, Italy

Start date

2015-05-16

End date

2015-05-17

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2015, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

MSR 2015 : Proceedings of the 12th Working Conference on Mining Software Repositories

Event

Mining Software Repositories. Working Conference (12th : 2015 : Florence, Italy)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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