- author = "Alexander E. I. Brownlee and Justyna Petke and Brad Alexander and Earl T. Barr and Markus Wagner and David R. White",
- title = "{Gin}: genetic improvement research made easy",
- booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference",
- year = "2019",
- editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre",
- isbn13 = "978-1-4503-6111-8",
- pages = "985--993",
- address = "Prague, Czech Republic",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "13-17 " # jul,
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, genetic improvement, Search-based Software Engineering, SBSE, Software engineering, Software notations and tools, GI",
-
URL = "
https://cs.adelaide.edu.au/users/markus/pub/2019gecco-gintool.pdf",
-
DOI = "
10.1145/3321707.3321841",
-
code_url = "
https://github.com/gintool/gin",
-
slides_url = "http://crest.cs.ucl.ac.uk/fileadmin/crest/COWphotos/Other/Petke.pdf",
- size = "9 pages",
- 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.",
-
notes = "Computing Science and Mathematics, University of
Stirling, Stirling, UK.
Also known as \cite{3321841} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)",
