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Broccoli: Bug localization with the help of text search engines

Benjamin Ledel,Steffen Herbold

Published 2021 in arXiv.org

ABSTRACT

Bug localization is a tedious activity in the bug fixing process in which a software developer tries to locate bugs in the source code described in a bug report. Since this process is time-consuming and requires additional knowledge about the software project, information retrieval techniques can aid the bug localization process. In this paper, we investigate if normal text search engines can improve existing bug localization approaches. In a case study, we evaluate the performance of our search engine approach Broccoli against seven state-of-the-art bug localization algorithms on 82 open source projects in two data sets. Our results show that including a search engine can increase the performance of the bug localization and that it is a useful extension to existing approaches. As part of our analysis we also exposed a flaw in a commonly used benchmark strategy, i.e., that files of a single release are considered. To increase the number of detectable files, we mitigate this flaw by considering the state of the software repository at the time of the bug report. Our results show that using single releases may lead to an underestimation of the the prediction performance.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    arXiv.org

  • Publication date

    2021-09-24

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 2109.11902
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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