- author = "Claire {Le Goues} and Westley Weimer and Stephanie Forrest",
- title = "Representations and operators for improving evolutionary software repair",
- booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference",
- year = "2012",
- editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Wil Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Rei and Jose Lozano and Martin Pelikan and Silja Meyer-Nienber and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
- isbn13 = "978-1-4503-1177-9",
- pages = "959--966",
- keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Software Engineering, Testing and Debugging, Artificial Intelligence, Search, Algorithms, Representation, crossover, mutation, search-based software engineering, software repair, GenProg, bug fixing",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Philadelphia, Pennsylvania, USA",
-
URL = "
http://www.clairelegoues.com.s3-website-us-east-1.amazonaws.com/docs/genprog-gecco2012-preprint.pdf",
-
DOI = "
10.1145/2330163.2330296",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- size = "8 pages",
- abstract = "Evolutionary computation is a promising technique for automating time-consuming and expensive software maintenance tasks, including bug repair. The success of this approach, however, depends at least partially on the choice of representation, fitness function, and operators. Previous work on evolutionary software repair has employed different approaches, but they have not yet been evaluated in depth. This paper investigates representation and operator choices for source-level evolutionary program repair in the GenProg framework [17], focusing on: (1) representation of individual variants, (2) crossover design, (3) mutation operators, and (4) search space definition. We evaluate empirically on a dataset comprising 8 C programs totalling over 5.1 million lines of code and containing 105 reproducible, human-confirmed defects. Our results provide concrete suggestions for operator and representation design choices for evolutionary program repair. When augmented to incorporate these suggestions, GenProg repairs 5 additional bugs (60 vs. 55 out of 105), with a decrease in repair time of 17--43percent for the more difficult repair searches.",
-
notes = "Bronze winner 2012 HUMIES GECCO 2012.
Also known as \cite{2330296} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)",
