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Predicting delays in software projects using networked classification

Conference Paper


Abstract


  • Software projects have a high risk of cost and schedule overruns, which has been a source of concern for the software engineering community for a long time. One of the challenges in software project management is to make reliable prediction of delays in the context of constant and rapid changes inherent in software projects. This paper presents a novel approach to providing automated support for project managers and other decision makers in predicting whether a subset of software tasks (among the hundreds to thousands of ongoing tasks) in a software project have a risk of being delayed. Our approach makes use of not only features specific to individual software tasks (i.e. local data) -- as done in previous work -- but also their relationships (i.e. networked data). In addition, using collective classification, our approach can simultaneously predict the degree of delay for a group of related tasks. Our evaluation results show a significant improvement over traditional approaches which perform classification on each task independently: achieving 46% -- 97% precision (49% improved), 46% -- 97% recall (28% improved), 56% -- 75% F-measure (39% improved), and 78% -- 95% Area Under the ROC Curve (16% improved).

UOW Authors


  •   Choetkiertikul, Morakot (external author)
  •   Dam, Hoa
  •   Tran, Truyen (external author)
  •   Ghose, Aditya

Publication Date


  • 2015

Citation


  • Choetkiertikul, M., Dam, H. Khanh., Tran, T. & Ghose, A. (2015). Predicting delays in software projects using networked classification. 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 353-364). United States: IEEE.

Scopus Eid


  • 2-s2.0-84963900777

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6145

Has Global Citation Frequency


Start Page


  • 353

End Page


  • 364

Place Of Publication


  • United States

Abstract


  • Software projects have a high risk of cost and schedule overruns, which has been a source of concern for the software engineering community for a long time. One of the challenges in software project management is to make reliable prediction of delays in the context of constant and rapid changes inherent in software projects. This paper presents a novel approach to providing automated support for project managers and other decision makers in predicting whether a subset of software tasks (among the hundreds to thousands of ongoing tasks) in a software project have a risk of being delayed. Our approach makes use of not only features specific to individual software tasks (i.e. local data) -- as done in previous work -- but also their relationships (i.e. networked data). In addition, using collective classification, our approach can simultaneously predict the degree of delay for a group of related tasks. Our evaluation results show a significant improvement over traditional approaches which perform classification on each task independently: achieving 46% -- 97% precision (49% improved), 46% -- 97% recall (28% improved), 56% -- 75% F-measure (39% improved), and 78% -- 95% Area Under the ROC Curve (16% improved).

UOW Authors


  •   Choetkiertikul, Morakot (external author)
  •   Dam, Hoa
  •   Tran, Truyen (external author)
  •   Ghose, Aditya

Publication Date


  • 2015

Citation


  • Choetkiertikul, M., Dam, H. Khanh., Tran, T. & Ghose, A. (2015). Predicting delays in software projects using networked classification. 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 353-364). United States: IEEE.

Scopus Eid


  • 2-s2.0-84963900777

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6145

Has Global Citation Frequency


Start Page


  • 353

End Page


  • 364

Place Of Publication


  • United States