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
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.