Clustering Dycom

Handle URI:
http://hdl.handle.net/10754/626153
Title:
Clustering Dycom
Authors:
Minku, Leandro L.; Hou, Siqing
Abstract:
Background: 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Minku LL, Hou S (2017) Clustering Dycom. Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering - PROMISE. Available: http://dx.doi.org/10.1145/3127005.3127007.
Publisher:
ACM Press
Journal:
Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering - PROMISE
Issue Date:
6-Oct-2017
DOI:
10.1145/3127005.3127007
Type:
Conference Paper
Sponsors:
Part of this work has been conducted during Siqing Hou’s internship at the University of Birmingham (UK)
Additional Links:
https://dl.acm.org/citation.cfm?doid=3127005.3127007
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMinku, Leandro L.en
dc.contributor.authorHou, Siqingen
dc.date.accessioned2017-11-14T12:46:06Z-
dc.date.available2017-11-14T12:46:06Z-
dc.date.issued2017-10-06en
dc.identifier.citationMinku LL, Hou S (2017) Clustering Dycom. Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering - PROMISE. Available: http://dx.doi.org/10.1145/3127005.3127007.en
dc.identifier.doi10.1145/3127005.3127007en
dc.identifier.urihttp://hdl.handle.net/10754/626153-
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.description.sponsorshipPart of this work has been conducted during Siqing Hou’s internship at the University of Birmingham (UK)en
dc.publisherACM Pressen
dc.relation.urlhttps://dl.acm.org/citation.cfm?doid=3127005.3127007en
dc.rightsArchived with thanks to Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering - PROMISEen
dc.titleClustering Dycomen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalProceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering - PROMISEen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Informatics, University of Leicester, Leicester, UKen
kaust.authorHou, Siqingen
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