ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3989

@Misc{2017arXiv171207804Y,
  author =       "Yuan Yuan2 and Wolfgang Banzhaf",
  title =        "{ARJA}: Automated Repair of Java Programs via
                 Multi-Objective Genetic Programming",
  howpublished = "arXiv:1712.07804",
  year =         "2017",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, Program repair, patch generation,
                 multi-objective optimization, SBSE, Software
                 Engineering",
  URL =          "https://arxiv.org/pdf/1712.07804",
  URL =          "http://adsabs.harvard.edu/abs/2017arXiv171207804Y",
  size =         "30 pages",
  abstract =     "Recent empirical studies show that the performance of
                 GenProg is not satisfactory, particularly for Java. In
                 this paper, we propose ARJA, a new GP based repair
                 approach for automated repair of Java programs. To be
                 specific, we present a novel lower-granularity patch
                 representation that properly decouples the search
                 subspaces of likely-buggy locations, operation types
                 and potential fix ingredients, enabling GP to explore
                 the search space more effectively. Based on this new
                 representation, we formulate automated program repair
                 as a multi-objective search problem and use NSGA-II to
                 look for simpler repairs. To reduce the computational
                 effort and search space, we introduce a test filtering
                 procedure that can speed up the fitness evaluation of
                 GP and three types of rules that can be applied to
                 avoid unnecessary manipulations of the code. Moreover,
                 we also propose a type matching strategy that can
                 create new potential fix ingredients by exploiting the
                 syntactic patterns of the existing statements. We
                 conduct a large-scale empirical evaluation of ARJA
                 along with its variants on both seeded bugs and
                 real-world bugs in comparison with several
                 state-of-the-art repair approaches. Our results verify
                 the effectiveness and efficiency of the search
                 mechanisms employed in ARJA and also show its
                 superiority over the other approaches. In particular,
                 compared to jGenProg (an implementation of GenProg for
                 Java), an ARJA version fully following the redundancy
                 assumption can generate a test-suite adequate patch for
                 more than twice the number of bugs (from 27 to 59), and
                 a correct patch for nearly four times of the number
                 (from 5 to 18), on 224 real-world bugs considered in
                 Defects4J. Furthermore, ARJA is able to correctly fix
                 several real multi-location bugs that are hard to be
                 repaired by most of the existing repair approaches.",
}

Genetic Programming entries for Yuan Yuan2 Wolfgang Banzhaf

Citations