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Permanent link (DOI): https://doi.org/10.7939/R3NZ80S36

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Mining the Temporal Evolution of the Android Bug Reporting Community via Sliding Windows Open Access

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Author or creator
Hindle, Abram
Jiang, Feng
Wang, Jiemin
Nascimento, Mario A.
Additional contributors
Subject/Keyword
Software Engineering
social network analysis
Type of item
Computing Science Technical Report
Computing science technical report ID
TR13-07
Language
English
Place
Time
Description
The open source development community consists of both paid and volunteer developers as well as new and experienced users. Previous work has applied social network analysis (SNA) to open source communities and has demonstrated value in expertise discovery and triaging. One problem with applying SNA directly to the data of the entire project lifetime is that the impact of local activities will be drowned out. In this paper we provide a method for aggregating, analyzing, and visualizing local (small time periods) interactions of bug reporting participants by using the SNA to measure the betweeness centrality of these participants. In particular we mined the Android bug repository by producing social networks from overlapping 30-day windows of bug reports, each sliding over by day. In this paper we define three patterns of participant behaviour based on their local centrality. We propose a method of analyzing the centrality of bug report participants both locally and globally, then we conduct a thorough case study of the bug reporters’ activity within the Android bug repository. Furthermore, we validate the conclusions of our method by mining the Android version control system and inspecting the Android release history. We found that windowed SNA analysis elicited local behaviour that were invisible during global analysis.
Date created
2013
DOI
doi:10.7939/R3NZ80S36
License information
Creative Commons Attribution 3.0 Unported
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