The York Research Database

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.