- author = "Francesco Marino and Giovanni Squillero and Alberto Tonda",
- title = "A General-Purpose Framework for Genetic Improvement",
- booktitle = "14th International Conference on Parallel Problem Solving from Nature",
- year = "2016",
- editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter",
- volume = "9921",
- series = "LNCS",
- pages = "345--352",
- address = "Edinburgh",
- month = "17-21 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Linear genetic programming Software engineering",
- isbn13 = "978-3-319-45823-6",
-
DOI = "
doi:10.1007/978-3-319-45823-6_32",
- size = "8 pages",
- abstract = "Genetic Improvement is an evolutionary-based technique. Despite its relatively recent introduction, several successful applications have been already reported in the scientific literature: it has been demonstrated able to modify the code complex programs without modifying their intended behaviour; to increase performance with regards to speed, energy consumption or memory use. Some results suggest that it could be also used to correct bugs, restoring the software's intended functionalities. Given the novelty of the technique, however, instances of Genetic Improvement so far rely upon ad-hoc, language-specific implementations. In this paper, we propose a general framework based on the software engineering's idea of mutation testing coupled with Genetic Programming, that can be easily adapted to different programming languages and objective. In a preliminary evaluation, the framework efficiently optimizes the code of the md5 hash function in C, Java, and Python.",
-
notes = "XML, mutation testing, MD5 microGP
http://ugp3.sourceforge.net/
PPSN2016",
