• DocumentCode
    3406857
  • Title

    A contextual approach towards more accurate duplicate bug report detection

  • Author

    Alipour, Anahita ; Hindle, Adrian ; Stroulia, Eleni

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Alberta, Edmonton, AB, Canada
  • fYear
    2013
  • fDate
    18-19 May 2013
  • Firstpage
    183
  • Lastpage
    192
  • Abstract
    Bug-tracking and issue-tracking systems tend to be populated with bugs, issues, or tickets written by a wide variety of bug reporters, with different levels of training and knowledge about the system being discussed. Many bug reporters lack the skills, vocabulary, knowledge, or time to efficiently search the issue tracker for similar issues. As a result, issue trackers are often full of duplicate issues and bugs, and bug triaging is time consuming and error prone. Many researchers have approached the bug-deduplication problem using off-the-shelf information-retrieval tools, such as BM25F used by Sun et al. In our work, we extend the state of the art by investigating how contextual information, relying on our prior knowledge of software quality, software architecture, and system-development (LDA) topics, can be exploited to improve bug-deduplication. We demonstrate the effectiveness of our contextual bug-deduplication method on the bug repository of the Android ecosystem. Based on this experience, we conclude that researchers should not ignore the context of software engineering when using IR tools for deduplication.
  • Keywords
    Linux; information retrieval; program debugging; software architecture; software quality; Android ecosystem; IR tools; LDA topics; bug repository; contextual bug-deduplication method; contextual information; duplicate bug report detection; information-retrieval tools; software architecture; software engineering; software quality; system-development; Accuracy; Androids; Computer bugs; Context; Humanoid robots; Software; Sun; contextual information; deduplication; duplicate bug reports; information retrieval; machine learning; textual similarity; triaging;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on
  • Conference_Location
    San Francisco, CA
  • ISSN
    2160-1852
  • Print_ISBN
    978-1-4799-0345-0
  • Type

    conf

  • DOI
    10.1109/MSR.2013.6624026
  • Filename
    6624026