Brownlee, AEI; Petke, J; Alexander, B; Barr, ET; Wagner, M; White, DR; (2019) Gin: Genetic Improvement Research Made Easy. In: López-Ibáñez, M and Auger, A and Stützle, T, (eds.) GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. (pp. pp. 985-993). Association for Computing Machinery (ACM): Prague, Czech Republic.

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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.

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