Combining Stochastic Grammars and Genetic Programming for Coverage Testing at the System Level

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Kifetew:2014:SSBSE,

• author = "Fitsum Meshesha Kifetew and Roberto Tiella and Paolo Tonella",
• title = "Combining Stochastic Grammars and Genetic Programming for Coverage Testing at the System Level",
• booktitle = "Proceedings of the 6th International Symposium, on Search-Based Software Engineering, SSBSE 2014",
• year = "2014",
• editor = "Claire {Le Goues} and Shin Yoo",
• volume = "8636",
• series = "LNCS",
• pages = "138--152",
• address = "Fortaleza, Brazil",
• month = "26-29 " # aug,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, SBSE, grammar based testing",
• isbn13 = "978-3-319-09939-2",
• URL = "http://www.springer.com/computer/swe/book/978-3-319-09939-2",
• DOI = "doi:10.1007/978-3-319-09940-8_10",
• size = "15 pages",
• abstract = "When tested at the system level, many programs require complex and highly structured inputs, which must typically satisfy some formal grammar. Existing techniques for grammar based testing make use of stochastic grammars that randomly derive test sentences from grammar productions, trying at the same time to avoid unbounded recursion. In this paper, we combine stochastic grammars with genetic programming, so as to take advantage of the guidance provided by a coverage oriented fitness function during the sentence derivation and evolution process. Experimental results show that the combination of stochastic grammars and genetic programming outperforms stochastic grammars alone.",
• notes = "StGP, EvoSuite, Calc, MDSL, JavaScript Rhino, branch coverage, GA/GP testsuite, test cases. Fitness using minimum branch distance (p148 'not particularly useful'?). Mutation testing. Grammar learning Lari+Young 1990. Beyene&Andrews ICST-2012",

}

Genetic Programming entries for Fitsum Meshesha Kifetew Roberto Tiella Paolo Tonella

Citations