Abstract
Genetic improvement (GI) is a young ield 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 modiiable 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 proiling. We show, through examples and a case study, how Gin facilitates experimentation and will speed innovation in GI.
Original language | English |
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Title of host publication | Proceedings of the 2019 Genetic and Evolutionary Computation Conference |
Editors | Anne Auger, Thomas Stützle |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 985-993 |
Number of pages | 9 |
ISBN (Electronic) | 9781450361118 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | The Genetic and Evolutionary Computation Conference 2019 - Prague, Czechia Duration: 13 Jul 2019 → 17 Jul 2019 Conference number: 21st https://gecco-2019.sigevo.org/index.html/HomePage https://dl.acm.org/doi/proceedings/10.1145/3321707 (Proceedings) |
Conference
Conference | The Genetic and Evolutionary Computation Conference 2019 |
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Abbreviated title | GECCO 2019 |
Country/Territory | Czechia |
City | Prague |
Period | 13/07/19 → 17/07/19 |
Internet address |
Keywords
- Genetic Improvement
- GI
- SBSE
- Search-based Software Engineering