The Optimisation of Stochastic Grammars to Enable Cost-Effective Probabilistic Structural Testing

The effectiveness of probabilistic structural testing depends on the characteristics of the probability distribution from which test inputs are sampled at random. Metaheuristic search has been shown to be a practical method of optimis- ing the characteristics of such distributions. However, the applicability of the existing search-based algorithm is lim- ited by the requirement that the software’s inputs must be a fixed number of numeric values. In this paper we relax this limitation by means of a new representation for the probability distribution. The repre- sentation is based on stochastic context-free grammars but incorporates two novel extensions: conditional production weights and the aggregation of terminal symbols represent- ing numeric values. We demonstrate that an algorithm which combines the new representation with hill-climbing search is able to effi- ciently derive probability distributions suitable for testing software with structurally-complex input domains.