Predicting failures with developer networks and social network analysis

  • Authors:

  • Andrew Meneely;Laurie Williams;Will Snipes;Jason Osborne

  • Affiliations:

  • North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;Nortel Networks, Research Triangle Park, NC;North Carolina State University, Raleigh, NC

  • Venue:
  • Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
  • Year:
  • 2008

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Abstract

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 file-based developer network metrics and failures.