Gin: Genetic improvement research made easy

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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.conferenceGenetic and Evolutionary Computation Conference (GECCO) (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic)
dc.contributor.editorLopezIbanez, M.
dc.date.issued2019
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.identifier.citationGECCO 2019: Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019 / LopezIbanez, M. (ed./s), pp.985-993
dc.identifier.doi10.1145/3321707.3321841
dc.identifier.isbn9781450361118
dc.identifier.orcidAlexander, B. [0000-0003-4118-2798]
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]
dc.identifier.urihttp://hdl.handle.net/2440/126367
dc.language.isoen
dc.publisherACM
dc.publisher.placeNew York
dc.relation.granthttp://purl.org/au-research/grants/arc/DE160100850
dc.rights© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.
dc.source.urihttps://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
pubs.publication-statusPublished

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