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 (Forthcoming)
Citation: Brownlee AEI, Petke J, Alexander B, Barr ET, Wagner M & White DR (2019) Gin: Genetic Improvement Research Made Easy (Forthcoming). In: Proceedings of the Conference on 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. https://doi.org/10.1145/3321707.3321841
Issue Date: 13-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
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