- author = "Yuan Yuan and Wolfgang Banzhaf",
- title = "A Hybrid Evolutionary System for Automatic Software Repair",
- 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 = "1417--1425",
- address = "Prague, Czech Republic",
-
DOI = "
doi:10.1145/3321707.3321830",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "13-17 " # jul,
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, EMO, APR, search-based software engineering, Program repair, evolutionary multi-objective optimization",
- size = "9 pages",
- abstract = "This paper presents an automatic software repair system that combines the characteristic components of several typical evolutionary computation based repair approaches into a unified repair framework so as to take advantage of their respective component strengths. We exploit both the redundancy assumption and repair templates to create a search space of candidate repairs. Then we employ a multi-objective evolutionary algorithm with a low-granularity patch representation to explore this search space, in order to find simple patches. In order to further reduce the search space and alleviate patch overfitting we introduce replacement similarity and insertion relevance to select more related statements as promising fix ingredients, and we adopt anti-patterns to customize the available operation types for each likely-buggy statement. We evaluate our system on 224 real bugs from the Defects4J dataset in comparison with the state-of-the-art repair approaches. The evaluation results show that the proposed system can fix 111 out of those 224 bugs in terms of passing all test cases, achieving substantial performance improvements over the state-of-the-art. Additionally, we demonstrate the ability of ARJA-e to fix multi-location bugs that are unlikely to be addressed by most of existing repair approaches.",
-
notes = "automatic bug fixing
Also known as \cite{3321830} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)",
