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. Attached Files Name Description MIMEType Size Downloads Title Predicting delays in software projects using networked classification Author(s) Choetikertikul, Morakot Dam, Hoa Khanh 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30081484 Document type: Conference Paper Collections: School of Information Technology Centre for Pattern Recognition and Data Analytics Open Access Checking Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 19 Feb 2016, 11:41:10 EST Fri, 19 Feb 2016, 11:42:33 EST Sat, 20 Feb 2016, 04:01:51 EST Fri, 26 Feb 2016, 18:04:14 EST Fri, 26 Feb 2016, 18:06:47 EST Mon, 07 Mar 2016, 07:24:16 EST Tue, 08 Mar 2016, 05:08:30 EST Tue, 26 Jul 2016, 14:13:50 EST Tue, 26 Jul 2016, 14:16:14 EST Wed, 12 Oct 2016, 14:52:22 EST Wed, 09 Nov 2016, 11:15:40 EST Wed, 09 Nov 2016, 11:42:36 EST Wed, 01 Mar 2017, 07:47:38 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 78 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 26 Feb 2016, 18:06:47 EST

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