Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/126367
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Type: | Conference paper |
Title: | Gin: Genetic improvement research made easy |
Author: | Brownlee, A.E.I. Petke, J. Alexander, B. Barr, E.T. Wagner, M. White, D.R. |
Citation: | GECCO 2019: Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019 / LopezIbanez, M. (ed./s), pp.985-993 |
Publisher: | ACM |
Publisher Place: | New York |
Issue Date: | 2019 |
ISBN: | 9781450361118 |
Conference Name: | Genetic and Evolutionary Computation Conference (GECCO) (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic) |
Editor: | LopezIbanez, M. |
Statement of Responsibility: | Alexander E. I. Brownlee, Justyna Petke, Brad Alexander, Earl T. Barr, Markus Wagner, David R. White |
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. |
Keywords: | Genetic Improvement; GI; Search-based Software Engineering; SBSE |
Rights: | © 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery. |
DOI: | 10.1145/3321707.3321841 |
Grant ID: | http://purl.org/au-research/grants/arc/DE160100850 |
Published version: | http://dx.doi.org/10.1145/3321707.3321841 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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