Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/40333
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMinku, Leandro L.-
dc.contributor.authorHou, Siqing-
dc.date.accessioned2017-09-06T13:27:41Z-
dc.date.available2017-11-08T02:45:07Z-
dc.date.issued2017-11-08-
dc.identifier.citationProceedings of The 13th International Conference on Predictive Models and Data Analytics in Software Engineering, November 8th 2017, Toronto, Canada.en
dc.identifier.isbn978-1-4503-5305-2-
dc.identifier.urihttps://dl.acm.org/citation.cfm?doid=3127005.3127007en
dc.identifier.urihttp://hdl.handle.net/2381/40333-
dc.description.abstractBackground: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom's predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom's CC subsets.en
dc.language.isoenen
dc.publisherACMen
dc.rightsCopyright © 2017, ACM. Deposited with reference to the publisher’s open access archiving policy.en
dc.titleClustering Dycom: An Online Cross-Company Software Effort Estimation Studyen
dc.typeConference Paperen
dc.identifier.doi10.1145/3127005.3127007-
dc.description.statusPeer-revieweden
dc.description.versionPost-printen
dc.description.presentedPromise'17, The 13th International Conference on Predictive Models and Data Analytics in Software Engineering, November 8th 2017, Toronto, Canada.en
dc.date.end2017-11-08-
dc.date.start2017-11-08-
pubs.organisational-group/Organisationen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERINGen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Scienceen
dc.dateaccepted2017-07-20-
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

Files in This Item:
File Description SizeFormat 
paper-revised-submitted.pdfPost-review (final submitted author manuscript)643.23 kBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.