Predicting the delay of issues with due dates in software projects

Choetkiertikul, Morakot, Dam, Hoa Khanh, Tran, Truyen and Ghose, Aditya 2017, Predicting the delay of issues with due dates in software projects, Empirical software engineering, vol. 22, no. 3, pp. 1223-1263, doi: 10.1007/s10664-016-9496-7.

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Title Predicting the delay of issues with due dates in software projects
Author(s) Choetkiertikul, Morakot
Dam, Hoa Khanh
Tran, TruyenORCID iD for Tran, Truyen
Ghose, Aditya
Journal name Empirical software engineering
Volume number 22
Issue number 3
Start page 1223
End page 1263
Total pages 41
Publisher Springer Science & Business Media
Place of publication New York, N.Y.
Publication date 2017-06
ISSN 1382-3256
Keyword(s) empirical software engineering
project management
mining software engineering repositories
Summary Issue-tracking systems (e.g. JIRA) have increasingly been used in many software projects. An issue could represent a software bug, a new requirement or a user story, or even a project task. A deadline can be imposed on an issue by either explicitly assigning a due date to it, or implicitly assigning it to a release and having it inherit the release’s deadline. This paper presents a novel approach to providing automated support for project managers and other decision makers in predicting whether an issue is at risk of being delayed against its deadline. A set of features (hereafter called risk factors) characterizing delayed issues were extracted from eight open source projects: Apache, Duraspace,, JBoss, JIRA, Moodle, Mulesoft, and WSO2. Risk factors with good discriminative power were selected to build predictive models to predict if the resolution of an issue will be at risk of being delayed. Our predictive models are able to predict both the the extend of the delay and the likelihood of the delay occurrence. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 79 % precision, 61 % recall, 68 % F-measure, and 83 % Area Under the ROC Curve. Our predictive models also have low error rates: on average 0.66 for Macro-averaged Mean Cost-Error and 0.72 Macro-averaged Mean Absolute Error.
Language eng
DOI 10.1007/s10664-016-9496-7
Field of Research 0803 Computer Software
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, Springer Science + Business Media
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