Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/40333
Title: Clustering Dycom: An Online Cross-Company Software Effort Estimation Study
Authors: Minku, Leandro L.
Hou, Siqing
First Published: 8-Nov-2017
Presented at: Promise'17, The 13th International Conference on Predictive Models and Data Analytics in Software Engineering, November 8th 2017, Toronto, Canada.
Start Date: 8-Nov-2017
End Date: 8-Nov-2017
Publisher: ACM
Citation: Proceedings of The 13th International Conference on Predictive Models and Data Analytics in Software Engineering, November 8th 2017, Toronto, Canada.
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.
DOI Link: 10.1145/3127005.3127007
ISBN: 978-1-4503-5305-2
Links: https://dl.acm.org/citation.cfm?doid=3127005.3127007
http://hdl.handle.net/2381/40333
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © 2017, ACM. Deposited with reference to the publisher’s open access archiving policy.
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

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