Conference Paper

JDF: Detecting Duplicate Bug Reports in Jazz

DOI: 10.1145/1810295.1810368 Conference: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2, ICSE 2010, Cape Town, South Africa, 1-8 May 2010
Source: DBLP


Both developers and users submit bug reports to a bug repository. These reports can help reveal defects and improve software quality. As the number of bug reports in a bug repository increases, the number of the potential duplicate bug reports increases. Detecting duplicate bug reports helps reduce development efforts in fixing defects. However, it is challenging to manually detect all potential duplicates because of the large number of existing bug reports. This paper presents JDF (representing Jazz Duplicate Finder), a tool that helps users to find potential duplicates of bug reports on Jazz, which is a team collaboration platform for software development and process management. JDF finds potential duplicates for a given bug report using natural language and execution information.

Download full-text


Available from: Tao Xie, Dec 01, 2014
    • "This approach scores bug reports which duplicate existing bug reports and provides them as top-N list. Song et al. [10] combined a similarity calculated from summary and description in bug reports (NL-S) and a similarity calculated from an execution trace in occurring a bug (E-S). NL-S is calculated from vectors which have TF-IDF value of each word in each bug report as their elements. "

    No preview · Conference Paper · Dec 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Testing refactoring engines is a challenging problem that has gained recent attention in research. Several techniques were proposed to automate generation of programs used as test inputs and to help developers in inspecting test failures. However, these techniques can require substantial effort for writing test generators or finding unique bugs, and do not provide an estimate of how reliable refactoring engines are for refactoring tasks on real software projects. This paper evaluates an end-to-end approach for testing refactoring engines and estimating their reliability by (1) systematically applying refactorings at a large number of places in well-known, open-source projects and collecting failures during refactoring or while trying to compile the refactored projects, (2) clustering failures into a small, manageable number of failure groups, and (3) inspecting failures to identify non-duplicate bugs. By using this approach on the Eclipse refactoring engines for Java and C, we already found and reported 77 new bugs for Java and 43 for C. Despite the seemingly large numbers of bugs, we found these refactoring engines to be relatively reliable, with only 1.4% of refactoring tasks failing for Java and 7.5% for C.
    Full-text · Conference Paper · Jul 2013

We use cookies to give you the best possible experience on ResearchGate. Read our cookies policy to learn more.