• DocumentCode
    625528
  • Title

    R2Fix: Automatically Generating Bug Fixes from Bug Reports

  • Author

    Chen Liu ; Jinqiu Yang ; Lin Tan ; Hafiz, Munawar

  • Author_Institution
    Univ. of Waterloo, Waterloo, ON, Canada
  • fYear
    2013
  • fDate
    18-22 March 2013
  • Firstpage
    282
  • Lastpage
    291
  • Abstract
    Many bugs, even those that are known and documented in bug reports, remain in mature software for a long time due to the lack of the development resources to fix them. We propose a general approach, R2Fix, to automatically generate bug-fixing patches from free-form bug reports. R2Fix combines past fix patterns, machine learning techniques, and semantic patch generation techniques to fix bugs automatically. We evaluate R2Fix on three projects, i.e., the Linux kernel, Mozilla, and Apache, for three important types of bugs: buffer overflows, null pointer bugs, and memory leaks. R2Fix generates 57 patches correctly, 5 of which are new patches for bugs that have not been fixed by developers yet. We reported all 5 new patches to the developers; 4 have already been accepted and committed to the code repositories. The 57 correct patches generated by R2Fix could have shortened and saved up to an average of 63 days of bug diagnosis and patch generation time.
  • Keywords
    learning (artificial intelligence); program debugging; Apache; Linux kernel; Mozilla; R2Fix; buffer overflows; bug diagnosis; bug fixes; bug-fixing patches; code repository; fix patterns; free-form bug reports; machine learning techniques; mature software; memory leaks; null pointer bugs; patch generation time; semantic patch generation techniques; Accuracy; Computer bugs; Generators; Kernel; Linux; Security; automated bug fixing; automated program repair; bug report classification; fix pattern study;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Testing, Verification and Validation (ICST), 2013 IEEE Sixth International Conference on
  • Conference_Location
    Luembourg
  • Print_ISBN
    978-1-4673-5961-0
  • Type

    conf

  • DOI
    10.1109/ICST.2013.24
  • Filename
    6569740