Abstract
Search-based testing generates test cases by encoding an adequacy criterion as the fitness function that drives a search-based optimization algorithm. Genetic algorithms have been successfully applied in search-based testing: while most of them use adequacy criteria based on the structure of the program, some try to maximize the mutation score of the test suite.
This work presents a genetic algorithm for generating a test suite for mutation testing. The algorithm adopts several features from existing bacteriological algorithms, using single test cases as individuals and keeping generated individuals in a memory. The algorithm can optionally use automated seeding when producing the first population, by taking into account interesting constants in the source code.
We have implemented this algorithm in a framework and we have applied it to a WS-BPEL composition, measuring to which extent the genetic algorithm improves the initial random test suite. We compare our genetic algorithm, with and without automated seeding, to random testing.
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Estero-Botaro, A., García-Domínguez, A., Domínguez-Jiménez, J.J., Palomo-Lozano, F., Medina-Bulo, I. (2014). A Framework for Genetic Test-Case Generation for WS-BPEL Compositions. In: Merayo, M.G., de Oca, E.M. (eds) Testing Software and Systems. ICTSS 2014. Lecture Notes in Computer Science, vol 8763. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44857-1_1
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