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Predicting delays in software projects using networked classification

Choetikertikul, Morakot, Dam, Hoa Khanh, Tran, Truyen and Ghose, Aditya 2015, Predicting delays in software projects using networked classification, in ASE 2015 : Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering, IEEE, Piscataway, N.J., pp. 353-364, doi: 10.1109/ASE.2015.55.

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Title Predicting delays in software projects using networked classification
Author(s) Choetikertikul, Morakot
Dam, Hoa Khanh
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Ghose, Aditya
Conference name Automated Software Engineering. International Conference (30th : 2015 : Lincoln, Nebraska)
Conference location Lincoln, Nebraska
Conference dates 9-13 Nov. 2015
Title of proceedings ASE 2015 : Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering
Publication date 2015
Start page 353
End page 364
Total pages 12
Publisher IEEE
Place of publication Piscataway, N.J.
Summary 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).
Language eng
DOI 10.1109/ASE.2015.55
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081484

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.