Extracting structural information from bug reports

  • Authors:

  • Nicolas Bettenburg;Rahul Premraj;Thomas Zimmermann;Sunghun Kim

  • Affiliations:

  • Saarland University, Saarbrücken, Germany;Saarland University, Saarbrücken, Germany;University of Calgary, Calgary, AB, Canada;Massachusetts Institute of Technology, Cambridge, MA, USA

  • Venue:
  • Proceedings of the 2008 international working conference on Mining software repositories
  • Year:
  • 2008

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Abstract

In software engineering experiments, the description of bug reports is typically treated as natural language text, although it often contains stack traces, source code, and patches. Neglecting such structural elements is a loss of valuable information; structure usually leads to a better performance of machine learning approaches. In this paper, we present a tool called infoZilla that detects structural elements from bug reports with near perfect accuracy and allows us to extract them. We anticipate that infoZilla can be used to leverage data from bug reports at a different granularity level that can facilitate interesting research in the future.