- author = "Justyna Petke and Alexander Brownlee",
- title = "Software Improvement with {Gin}: a Case Study",
- booktitle = "SSBSE 2019",
- year = "2019",
- editor = "Shiva Nejati and Gregory Gay",
- volume = "11664",
- series = "LNCS",
- pages = "183--189",
- address = "Tallinn, Estonia",
- month = "31 " # aug # " - 1 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, genetic improvement, Search-Based Software Engineering, SBSE, NFR",
- isbn13 = "978-3-030-27454-2",
-
URL = "
http://discovery.ucl.ac.uk/10077237/",
-
URL = "
https://dspace.stir.ac.uk/bitstream/1893/30334/1/Software_Improvement_with_Gin__a_Case_Study.pdf",
-
DOI = "
doi:10.1007/978-3-030-27455-9_14",
- size = "6 pages",
- abstract = "We provide a case study for the usage of Gin, a genetic improvement toolbox for Java. In particular, we implemented a simple GP search and targeted two software optimisation properties: runtime and repair. We ran our search algorithm on Gson, a Java library for converting Java objects to JSON and vice-versa. We report on runtime improvements and fixes found. We provide all the new code and data on the dedicated website: https://github.com/justynapt/ssbseChallenge2019.",
-
notes = "JUnit, PIT maven, \cite{Brownlee:2019:GECCO}. Delete,
copy, swap and replace operations plus micro-mutations
\cite{Haraldsson:2017:EuroGP}. pop=21,
gens=10.
Profiled with hprof which shows only 585 of 1050 tests on the hprof call stack. Best patch improved runtime by 24percent but doubt that this was the intended behaviour.
existing test suite is not enough to capture software behaviour, perhaps Gin can thus be used to test test suites.
http://ssbse19.mines-albi.fr/",
