Using bug report similarity to enhance bug localisation

Steven Davies*, Marc Roper, Murray Wood

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Bug localisation techniques are proposed as a method to reduce the time developers spend on maintenance, allowing them to quickly find source code relevant to a bug. Some techniques are based on information retrieval methods, treating the source code as a corpus and the bug report as a query. While these have shown success, there remain a number of little-exploited additional sources of information which could enhance the techniques, including the textual similarity between bug reports themselves. Based on successful results in detecting duplicate bug reports, this work asks: if duplicate bugs reports, which by definition are fixed in the same source location, can be detected through the use of similar language, can bugs which are in the same location but not duplicates be detected in the same way? A technique using this information is implemented and evaluated on 372 bugs across 4 projects, and is found to improve performance on all projects. In particular, the technique increases the number of bugs where the first relevant method presented to developers is the first result from 6 to 27, and those in the top-10 from 50 to 57, showing that it can be successfully used to enhance existing bug localisation techniques.

Original languageEnglish
Title of host publicationProceedings - 19th Working Conference on Reverse Engineering, WCRE 2012
Pages125-134
Number of pages10
DOIs
Publication statusPublished - 20 Dec 2012
Event19th Working Conference on Reverse Engineering, WCRE 2012 - Kingston, ON, Canada
Duration: 15 Oct 201218 Oct 2012

Publication series

NameProceedings - Working Conference on Reverse Engineering, WCRE
ISSN (Print)1095-1350

Conference

Conference19th Working Conference on Reverse Engineering, WCRE 2012
Country/TerritoryCanada
CityKingston, ON
Period15/10/1218/10/12

Keywords / Materials (for Non-textual outputs)

  • bug localisation
  • feature location
  • mining software repositories

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