- author = "James Callan and Justyna Petke",
- title = "Multi-objective Genetic Improvement: A Case Study with {EvoSuite}",
- booktitle = "14th International Symposium on Search Based Software Engineering SSBSE 2020",
- year = "2022",
- editor = "Mike Papadakis and Silvia Regina Vergilio",
- series = "LNCS",
- volume = "13711",
- pages = "111--117",
- address = "Singapore",
- month = "17-18 " # nov,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Genetic Improvement, GIN, NSGA2",
- isbn13 = "978-3-031-21251-2",
-
DOI = "
doi:10.1007/978-3-031-21251-2_8",
- size = "16 pages",
- abstract = "Automated multi-objective software optimisation offers an attractive solution to software developers wanting to balance often conflicting objectives, such as memory consumption and execution time. Work on using multi-objective search-based approaches to optimise for such non-functional software behaviour has so far been scarce, with tooling unavailable for use. To fill this gap we extended an existing generalist, open source, genetic improvement tool, Gin, with a multi-objective search strategy, NSGA-II. We ran our implementation on a mature, large software to show its use. In particular, we chose EvoSuite, a tool for automatic test case generation for Java. We use our multi-objective extension of Gin to improve both the execution time and memory usage of EvoSuite. We find improvements to execution time of up to 77.8percent and improvements to memory of up to 9.2percent on our test set. We also release our code, providing the first open source multi-objective genetic improvement tooling for improvement of memory and runtime for Java.",
-
notes = "Challenge
Track
https://conf.researchr.org/home/ssbse-2022",
