Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing

Software-based optimization techniques have been increasingly used to automate code coverage analysis since the nineties. Although several studies suggest that interdependencies can exist between condition constructs in branching conditions of real life programs e.g. ((formula presented)) or ((formula presented)), etc., to date, only the Ariadne system, a Grammatical Evolution (GE)-based Search Based Software Testing (SBST) technique, exploits interdependencies between variables to efficiently automate code coverage analysis. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to very efficiently evolve highly complex test cases, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from the interdependencies involving constants e.g. ((formula presented)), which are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. We propose to seed the grammar with constants extracted from the source code of the program under test in order to enhance and extend Ariadne’s capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude.