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Research on software project developer behaviors with K-means clustering analysis
Conference proceeding   Open access   Peer reviewed

Research on software project developer behaviors with K-means clustering analysis

Joint Proceedings of the Inforte Summer School on Software Maintenance and Evolution (SSSME-2019), Vol.2520, pp.54-61
CEUR Workshop Proceedings, 2520
Summer School on Software Maintenance and Evolution (Tampere, 02/09/2019–04/09/2019)
2019
Handle:
https://hdl.handle.net/10863/52191

Abstract

Clustering Code smell Community smell Developer behaviors K-means Technical debt Data Mining
Research on technical debt and community smell have drawn increasing attention in the academia of software engineering in the latest decade. Furthermore, data mining methods have been widely applied in the very domain as well. However, limited studies have contribute to the understanding of software project community using data mining methods, especially regarding the analysis of developer behaviors. Using K-means clustering, this study provides a preliminary analysis on the classification of open source software project developers based on the statistics of their behaviors related to technical debts. The results show that developers can be categorized into three different behavior groups, including, Veterans, Vulnerability Creators, and Fault Inducers/ Commoners.
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http://www.scopus.com/inward/record.url?eid=2-s2.0-85077510435&partnerID=MN8TOARSView
url
https://ceur-ws.org/Vol-2520/paper2b.pdfView

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