Mining the Temporal Evolution of the Android Bug Reporting Community via Sliding Windows
Feng Jiang, Jiemin Wang, Abram Hindle, Mario A. Nascimento

TL;DR
This paper introduces a sliding window social network analysis method to study local interactions in the Android bug reporting community, revealing behaviors hidden in global analysis and linking them to release history.
Contribution
It proposes a novel sliding window SNA approach for analyzing local participant behavior in open source communities, validated through a case study on Android.
Findings
Local behaviors are visible with windowed SNA but not in global analysis
The method uncovers patterns linked to Android release cycles
Sliding window analysis enhances understanding of community dynamics
Abstract
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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Web Data Mining and Analysis · Software System Performance and Reliability
