Boosting Search-Based Testing by using Constraint-based Testing. Sakti, A., Gu�h�neuc, Y., & Pesant, G. In Fraser, G. & de Souza, J. T., editors, Proceedings of the 4<sup>th</sup> Symposium on Search Based Software Engineering (SSBSE), pages 213–227, September, 2012. IEEE CS Press. 15 pages.
Boosting Search-Based Testing by using Constraint-based Testing [pdf]Paper  abstract   bibtex   
Search-based testing (SBT) uses an evolutionary algorithm in order to generate test cases. In general for search-based testing the initial population is generated using a random selection. Such an initial population is likely to achieve low coverage. A guided selection procedure to generate a diversified initial population may substantially increase the chance of reaching adequate coverage with less effort, therefore saving in resource expenditure. In this paper we propose an approach that models a relaxed version of the unit under test as a constraint satisfaction problem. Based on this model and the test target we generate an initial population. An evolutionary algorithm uses this population to generate test input leading to cover the test target. The approach combines constraint-based and search-based techniques and has two key advantages: It does not require any change in the underlying testing techniques and it avoids traditional problems associated either with constraint-based or search-based testing. Using eToc, an open source SBT tool, a prototype of this approach has been implemented. Empirical results on both known benchmarks and two open source programs are presented.
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