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

Full Implementation of an Estimation of Distribution Algorithm on a GPU

We submit an implementation of an Estimation of Distribution Algorithm – specifically a variant of the Bayesian Optimisation Algorithm (BOA) – using GPGPU. Every aspect of the algorithm is executed on the device, and it makes effective of use multiple GPU devices in a single machine. As for other EDAs, our implementation is generic in that it may be applied to any problem for which solutions may be represented as binary strings. For the purpose of this paper, we apply it to a particular problem known to be difficult for metaheuristic algorithms due to high interdependency between variables: finding the lowest energy state of an Ising Spin Glass. We show that our GPU implementation demonstrates a speedup in excess of 80x compared with an equivalent CPU implementation. To our knowledge, this is the first EDA to be implemented fully on the GPU