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.
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Kifetew, F.M., Tiella, R., Tonella, P. (2014). Combining Stochastic Grammars and Genetic Programming for Coverage Testing at the System Level. In: Le Goues, C., Yoo, S. (eds) Search-Based Software Engineering. SSBSE 2014. Lecture Notes in Computer Science, vol 8636. Springer, Cham. https://doi.org/10.1007/978-3-319-09940-8_10
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DOI: https://doi.org/10.1007/978-3-319-09940-8_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09939-2
Online ISBN: 978-3-319-09940-8
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