|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Author(s):||Brownlee, Alexander E I|
Barr, Earl T
White, David R.
|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|
|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:||This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.|
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