Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/126367
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dc.contributor.authorBrownlee, A.E.I.-
dc.contributor.authorPetke, J.-
dc.contributor.authorAlexander, B.-
dc.contributor.authorBarr, E.T.-
dc.contributor.authorWagner, M.-
dc.contributor.authorWhite, D.R.-
dc.contributor.editorLopezIbanez, M.-
dc.date.issued2019-
dc.identifier.citationGECCO 2019: Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019 / LopezIbanez, M. (ed./s), pp.985-993-
dc.identifier.isbn9781450361118-
dc.identifier.urihttp://hdl.handle.net/2440/126367-
dc.description.abstractGenetic 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.statementofresponsibilityAlexander E. I. Brownlee, Justyna Petke, Brad Alexander, Earl T. Barr, Markus Wagner, David R. White-
dc.language.isoen-
dc.publisherACM-
dc.rights© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.-
dc.source.urihttp://dx.doi.org/10.1145/3321707.3321841-
dc.subjectGenetic Improvement; GI; Search-based Software Engineering; SBSE-
dc.titleGin: Genetic improvement research made easy-
dc.typeConference paper-
dc.contributor.conferenceGenetic and Evolutionary Computation Conference (GECCO) (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic)-
dc.identifier.doi10.1145/3321707.3321841-
dc.publisher.placeNew York-
dc.relation.granthttp://purl.org/au-research/grants/arc/DE160100850-
pubs.publication-statusPublished-
dc.identifier.orcidAlexander, B. [0000-0003-4118-2798]-
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]-
Appears in Collections:Aurora harvest 4
Computer Science publications

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