STATEMATE applied to statistical software testing
ISSTA '93 Proceedings of the 1993 ACM SIGSOFT international symposium on Software testing and analysis
Using production grammars in software testing
Proceedings of the 2nd conference on Domain-specific languages
Generating Test Data with Enhanced Context-Free Grammars
IEEE Software
A New Way of Automating Statistical Testing Methods
Proceedings of the 16th IEEE international conference on Automated software engineering
Empirical Software Engineering
Grammar-based whitebox fuzzing
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Efficient Software Verification: Statistical Testing Using Automated Search
IEEE Transactions on Software Engineering
A Principled Evaluation of the Effect of Directed Mutation on Search-Based Statistical Testing
ICSTW '11 Proceedings of the 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops
Generating String Test Data for Code Coverage
ICST '12 Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation
Search methodologies in real-world software engineering
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
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 optimising the characteristics of such distributions. However, the applicability of the existing search-based algorithm is limited 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 representation is based on stochastic context-free grammars but incorporates two novel extensions: conditional production weights and the aggregation of terminal symbols representing numeric values. We demonstrate that an algorithm which combines the new representation with hill-climbing search is able to efficiently derive probability distributions suitable for testing software with structurally-complex input domains.