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SIGSOFT
2008
ACM

Predicting failures with developer networks and social network analysis

9 years 12 months ago
Predicting failures with developer networks and social network analysis
Software fails and fixing it is expensive. Research in failure prediction has been highly successful at modeling software failures. Few models, however, consider the key cause of failures in software: people. Understanding the structure of developer collaboration could explain a lot about the reliability of the final product. We examine this collaboration structure with the developer network derived from code churn information that can predict failures at the file level. We conducted a case study involving a mature Nortel networking product of over three million lines of code. Failure prediction models were developed using test and post-release failure data from two releases, then validated against a subsequent release. One model's prioritization revealed 58% of the failures in 20% of the files compared with the optimal prioritization that would have found 61% in 20% of the files, indicating that a significant correlation exists between filebased developer network metrics and fai...
Andrew Meneely, Laurie Williams, Will Snipes, Jaso
Added 20 Nov 2009
Updated 20 Nov 2009
Type Conference
Year 2008
Where SIGSOFT
Authors Andrew Meneely, Laurie Williams, Will Snipes, Jason Osborne
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