Please use this identifier to cite or link to this item: http://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 / 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)
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.
RMID: 1000000543
DOI: 10.1145/3321707.3321841
Grant ID: http://purl.org/au-research/grants/arc/DE160100850
Appears in Collections:Computer Science publications

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