Automatic Patch Generation Learned from Human-Written Patches

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Dongsun Kim and Jaechang Nam and Jaewoo Song and 
                 Sunghun Kim",
  title =        "Automatic Patch Generation Learned from Human-Written
  booktitle =    "35th International Conference on Software Engineering
                 (ICSE 2013)",
  year =         "2013",
  pages =        "802--811",
  address =      "San Francisco, USA",
  month =        "18-26 " # may,
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 software maintenance, user interfaces, GenProg, genetic
                 programming based patch generation, pattern based
                 automatic program repair, software maintenance task,
                 software systems, Computer bugs, Fault location",
  isbn13 =       "978-1-4673-3073-2",
  DOI =          "doi:10.1109/ICSE.2013.6606626",
  size =         "10 pages",
  abstract =     "Patch generation is an essential software maintenance
                 task because most software systems inevitably have bugs
                 that need to be fixed. Unfortunately, human resources
                 are often insufficient to fix all reported and known
                 bugs. To address this issue, several automated patch
                 generation techniques have been proposed. In
                 particular, a genetic-programming-based patch
                 generation technique, GenProg, proposed by Weimer et
                 al., has shown promising results. However, these
                 techniques can generate nonsensical patches due to the
                 randomness of their mutation operations.

                 To address this limitation, we propose a novel patch
                 generation approach, Pattern-based Automatic program
                 Repair (Par), using fix patterns learnt from existing
                 human-written patches. We manually inspected more than
                 60,000 human-written patches and found there are
                 several common fix patterns. Our approach leverages
                 these fix patterns to generate program patches
                 automatically. We experimentally evaluated Par on 119
                 real bugs. In addition, a user study involving 89
                 students and 164 developers confirmed that patches
                 generated by our approach are more acceptable than
                 those generated by GenProg. Par successfully generated
                 patches for 27 out of 119 bugs, while GenProg was
                 successful for only 16 bugs.",
  notes =        "Cited by

                 Also known as \cite{6606626}",

Genetic Programming entries for Dongsun Kim Jaechang Nam Jaewoo Song Sunghun Kim