13-11-2012, 03:02 PM
Input Domain Reduction through Irrelevant Variable Removal and Its Effect on Local, Global, and Hybrid Search- Based
ABSTRACT
Search-Based Test Data Generation reformulates testing
goals as fitness functions so that test input generation
can be automated by some chosen search-based
optimization algorithm. The optimization algorithm
searches the space of potential inputs, seeking those that
are “fit for purpose,” guided by the fitness function. The
search space of potential inputs can be very large, even
for very small systems under test. Its size is, of course, a
key determining factor affecting the performance of any
search-based approach. However, despite the large
volume of work on Search-Based Software Testing, the
literature contains little that concerns the performance
impact of search space reduction. This paper proposes a
static dependence analysis derived from program slicing
that can be used to support search space reduction. The
paper presents both a theoretical and empirical analysis
of the application of this approach to open source and
industrial production code. The results provide evidence
to support the claim that input domain reduction has a
significant effect on the performance of local, global,
and hybrid search, while a purely random search is
unaffected.