Predicting the delay of issues with due dates in software projects View Full Text


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Article Info

DATE

2017-01-19

AUTHORS

Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose

ABSTRACT

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, Java.net, 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. More... »

PAGES

1223-1263

References to SciGraph publications

  • 2013. Profiling Event Logs to Configure Risk Indicators for Process Delays in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2004-10. A Survey of Outlier Detection Methodologies in ARTIFICIAL INTELLIGENCE REVIEW
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2001-08-30. Understanding Probabilistic Classifiers in MACHINE LEARNING: ECML 2001
  • 2005. Combining Classifiers in Software Quality Prediction: A Neural Network Approach in ADVANCES IN NEURAL NETWORKS – ISNN 2005
  • 2012-09-20. Studying re-opened bugs in open source software in EMPIRICAL SOFTWARE ENGINEERING
  • 2014-09-18. Automatic, high accuracy prediction of reopened bugs in AUTOMATED SOFTWARE ENGINEERING
  • 2014-08-03. Automated prediction of bug report priority using multi-factor analysis in EMPIRICAL SOFTWARE ENGINEERING
  • 1989. Software risk management in ESEC '89
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10664-016-9496-7

    DOI

    http://dx.doi.org/10.1007/s10664-016-9496-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1039171695


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