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.
|Title of host publication||Testing software and systems|
|Subtitle of host publication||26th IFIP WG 6.1 International Conference, ICTSS 2014, Madrid, Spain, September 23-25, 2014. Proceedings|
|Editors||Mercedes G. Merayo, Edgardo Montes de Oca|
|Place of Publication||Berlin (DE)|
|Number of pages||16|
|Publication status||Published - 2014|
|Event||26th IFIP WG 6.1 International Conference - Madrid, Spain|
Duration: 23 Sep 2014 → 25 Sep 2014
|Name||Lecture Notes in Computer Science|
|Conference||26th IFIP WG 6.1 International Conference|
|Abbreviated title||ICTSS 2014|
|Period||23/09/14 → 25/09/14|