Automatic Program Repair Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{LeGoues:thesis,
  author =       "Claire {Le Goues}",
  title =        "Automatic Program Repair Using Genetic Programming",
  school =       "Faculty of the School of Engineering and Applied
                 Science, University of Virginia",
  year =         "2013",
  address =      "USA",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, SBSE",
  URL =          "http://www.cs.virginia.edu/~weimer/students/claire-phd.pdf",
  size =         "146 pages",
  abstract =     "Software quality is an urgent problem. There are so
                 many bugs in industrial program source code that mature
                 software projects are known to ship with both known and
                 unknown bugs [1], and the number of outstanding defects
                 typically exceeds the resources available to address
                 them [2]. This has become a pressing economic problem
                 whose costs in the United States can be measured in the
                 billions of dollars annually [3].

                 A dominant reason that software defects are so
                 expensive is that fixing them remains a manual process.
                 The process of identifying, triaging, reproducing, and
                 localising a particular bug, coupled with the task of
                 understanding the underlying error, identifying a set
                 of code changes that address it correctly, and then
                 verifying those changes, costs both time [4] and money,
                 and the cost of repairing a defect can increase by
                 orders of magnitude as development progresses [5]. As a
                 result, many defects, including critical security
                 defects [6], remain unaddressed for long periods of
                 time [7]. Moreover, humans are error-prone, and many
                 human fixes are imperfect, in that they are either
                 incorrect or lead to crashes, hangs, corruption, or
                 security problems [8]. As a result, defect repair has
                 become a major component of software maintenance, which
                 in turn consumes up to 90% of the total lifecycle cost
                 of a given piece of software [9].

                 Although considerable research attention has been paid
                 to supporting various aspects of the manual debugging
                 process [10, 11], and also to preempting or dynamically
                 addressing particular classes of vulnerabilities, such
                 as buffer overruns [12, 13], there exist virtually no
                 previous automated solutions that address the synthesis
                 of patches for general bugs as they are reported in
                 real-world software.

                 The primary contribution of this dissertation is
                 GenProg, one of the very first automatic solutions
                 designed to help alleviate the manual bug repair burden
                 by automatically and generically patching bugs in
                 deployed and legacy software. GenProg uses a novel
                 genetic programming algorithm, guided by test cases and
                 domain-specific operators, to affect scalable,
                 expressive, and high quality automated repair. We
                 present experimental evidence to substantiate our
                 claims that GenProg can repair multiple types of bugs
                 in multiple types of programs, and that it can repair a
                 large proportion of the bugs that human developers
                 address in practice (that it is expressive); that it
                 scales to real-world system sizes (that it is
                 scalable); and that it produces repairs that are of
                 sufficiently high quality. Over the course of this
                 evaluation, we contribute new benchmark sets of real
                 bugs in real open-source software and novel
                 experimental frameworks for quantitatively evaluating
                 an automated repair technique. We also contribute a
                 novel characterisation of the automated repair search
                 space, and provide analysis both of that space and of
                 the performance and scaling behaviour of our
                 technique.

                 General automated software repair was unheard of in
                 2009. In 2013, it has its own multi-paper sessions in
                 top tier software engineering conferences. The research
                 area shows no signs of slowing down. This
                 dissertation's description of GenProg provides a
                 detailed report on the state of the art for early
                 automated software repair efforts.",
  notes =        "Supervisor Wesley R. Weimer",
}

Genetic Programming entries for Claire Le Goues

Citations