Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/126367
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dc.contributor.author | Brownlee, A.E.I. | - |
dc.contributor.author | Petke, J. | - |
dc.contributor.author | Alexander, B. | - |
dc.contributor.author | Barr, E.T. | - |
dc.contributor.author | Wagner, M. | - |
dc.contributor.author | White, D.R. | - |
dc.contributor.editor | LopezIbanez, M. | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | GECCO 2019: Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019 / LopezIbanez, M. (ed./s), pp.985-993 | - |
dc.identifier.isbn | 9781450361118 | - |
dc.identifier.uri | http://hdl.handle.net/2440/126367 | - |
dc.description.abstract | Genetic improvement (GI) is a young field of research on the cusp of transforming software development. GI uses search to improve existing software. Researchers have already shown that GI can improve human-written code, ranging from program repair to optimising run-time, from reducing energy-consumption to the transplantation of new functionality. Much remains to be done. The cost of re-implementing GI to investigate new approaches is hindering progress. Therefore, we present Gin, an extensible and modifiable toolbox for GI experimentation, with a novel combination of features. Instantiated in Java and targeting the Java ecosystem, Gin automatically transforms, builds, and tests Java projects. Out of the box, Gin supports automated test-generation and source code profiling. We show, through examples and a case study, how Gin facilitates experimentation and will speed innovation in GI. | - |
dc.description.statementofresponsibility | Alexander E. I. Brownlee, Justyna Petke, Brad Alexander, Earl T. Barr, Markus Wagner, David R. White | - |
dc.language.iso | en | - |
dc.publisher | ACM | - |
dc.rights | © 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery. | - |
dc.source.uri | http://dx.doi.org/10.1145/3321707.3321841 | - |
dc.subject | Genetic Improvement; GI; Search-based Software Engineering; SBSE | - |
dc.title | Gin: Genetic improvement research made easy | - |
dc.type | Conference paper | - |
dc.contributor.conference | Genetic and Evolutionary Computation Conference (GECCO) (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic) | - |
dc.identifier.doi | 10.1145/3321707.3321841 | - |
dc.publisher.place | New York | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DE160100850 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Alexander, B. [0000-0003-4118-2798] | - |
dc.identifier.orcid | Wagner, M. [0000-0002-3124-0061] | - |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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