Mining collaboration patterns from a large developer network

Didi Surian*, David Lo, Ee Peng Lim

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

36 Citations (Scopus)

Abstract

In this study, we extract patterns from a large developer collaborations network extracted from SourceForge.Net at high and low level of details. At the high level of details, we extract various network-level statistics from the network. At the low level of details, we extract topological sub-graph patterns that are frequently seen among collaborating developers. Extracting subgraph patterns from large graphs is a hard NP-complete problem. To address this challenge, we employ a novel combination of graph mining and graph matching by leveraging network-level properties of a developer network. With the approach, we successfully analyze a snapshot of SourceForge.Net data taken on September 2009. We present mined patterns and describe interesting observations.

Original languageEnglish
Title of host publicationProceedings - 17th Working Conference on Reverse Engineering, WCRE 2010
EditorsGuiliano Antoniol, Martin Pinzger, Elliot Chikofsky
Place of PublicationBeverly, MA, United States
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages269-273
Number of pages5
ISBN (Electronic)9780769541235
ISBN (Print)9781424489114
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event17th Working Conference on Reverse Engineering, WCRE 2010 - Beverly, MA, United States
Duration: 13 Oct 201016 Oct 2010

Other

Other17th Working Conference on Reverse Engineering, WCRE 2010
CountryUnited States
CityBeverly, MA
Period13/10/1016/10/10

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