Brownlee, A.E.I.Petke, J.Alexander, B.Barr, E.T.Wagner, M.White, D.R.LopezIbanez, M.2020-07-032020-07-032019GECCO 2019: Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019 / LopezIbanez, M. (ed./s), pp.985-9939781450361118http://hdl.handle.net/2440/126367Genetic 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.en© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.Genetic Improvement; GI; Search-based Software Engineering; SBSEConference paper100000054310.1145/3321707.33218410005232184001162-s2.0-85072334966498128Alexander, B. [0000-0003-4118-2798]Wagner, M. [0000-0002-3124-0061]