Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29352
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Brownlee, Alexander E I
Petke, Justyna
Alexander, Brad
Barr, Earl T
Wagner, Markus
White, David R.
Contact Email: alexander.brownlee@stir.ac.uk
Title: Gin: Genetic Improvement Research Made Easy
Citation: Brownlee AEI, Petke J, Alexander B, Barr ET, Wagner M & White DR (2019) Gin: Genetic Improvement Research Made Easy. In: GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2019: The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: ACM, pp. 985-993. https://doi.org/10.1145/3321707.3321841
Issue Date: Jul-2019
Conference Name: GECCO 2019: The Genetic and Evolutionary Computation Conference
Conference Dates: 2019-07-13 - 2019-07-17
Conference Location: Prague, Czech Republic
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.
Status: AM - Accepted Manuscript
Rights: © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2019: The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: ACM, pp. 985-993. http://doi.acm.org/10.1145/3321707.3321841

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