- author = "Omar M. Villanueva and Leonardo Trujillo and Daniel E Hernandez",
- title = "Novelty Search for Automatic Bug Repair",
- year = "2020",
- editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann",
- isbn13 = "9781450371285",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
-
URL = "
https://doi.org/10.1145/3377930.3389845",
-
DOI = "
doi:10.1145/3377930.3389845",
- booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference",
- pages = "1021--1028",
- size = "8 pages",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement, APR, GenProg, novelty search",
- address = "internet",
- series = "GECCO '20",
- month = jul # " 8-12",
- organisation = "SIGEVO",
- abstract = "Genetic Improvement (GI) focuses on the development of evolutionary methods to automate software engineering tasks, such as performance improvement or software bugs removal. Concerning the latter, one of the earliest and most well-known methods in this area is the Genetic Program Repair (GenProg), a variant of Genetic Programming (GP). However, most GI systems encounter problems that are derived from the fact that they operate directly at source code level. These problems include highly neutral fitness landscapes and loss of diversity during the search, which are always undesirable in search and optimization tasks. This paper explores the use of Novelty Search (NS) with GenProg, since it can allow a search process to overcome these type of issues. While NS has been combined with GP before, and recently used with other GI systems, in the area of automatic bug repair NS has not been used until this work. Results show that GenProg with NS outperforms the original algorithm in some cases, based on an extensive experimental evaluation.",
-
notes = "See also \cite{Trujillo:2021:IEEESoftware}
manybugs benchmark v GenProg
Also known as \cite{10.1145/3377930.3389845} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
