Poulding, S., Alexander, R. orcid.org/0000-0003-3818-0310, Clark, J.A. orcid.org/0000-0002-9230-9739 et al. (1 more author) (2015) The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing. Journal of Systems and Software. pp. 296-310. ISSN 0164-1212
Abstract
Abstract The effectiveness of statistical testing, a probabilistic structural testing strategy, depends on the characteristics of the probability distribution from which test inputs are sampled. 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 ordinal values. In this paper we propose a new algorithm that relaxes this limitation and so permits the derivation of probability distributions for a much wider range of software. The representation used by the new algorithm is based on a stochastic grammar supplemented with two novel features: conditional production weights and the dynamic partitioning of ordinal ranges. We demonstrate empirically that a search algorithm using this representation can optimise probability distributions over complex input domains and thereby enable cost-effective statistical testing, and that the use of both conditional production weights and dynamic partitioning can be beneficial to the search process.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Grammar-based testing |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 12 Jul 2018 09:00 |
Last Modified: | 19 Nov 2024 00:29 |
Published Version: | https://doi.org/10.1016/j.jss.2014.11.042 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.jss.2014.11.042 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132851 |
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