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The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing

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The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing. / Poulding, S.; Alexander, R.; Clark, J.A.; Hadley, M.J.

In: Journal of Systems and Software, Vol. 103, 01.05.2015, p. 296-310.

Research output: Contribution to journalArticlepeer-review

Harvard

Poulding, S, Alexander, R, Clark, JA & Hadley, MJ 2015, 'The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing', Journal of Systems and Software, vol. 103, pp. 296-310. https://doi.org/10.1016/j.jss.2014.11.042

APA

Poulding, S., Alexander, R., Clark, J. A., & Hadley, M. J. (2015). The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing. Journal of Systems and Software, 103, 296-310. https://doi.org/10.1016/j.jss.2014.11.042

Vancouver

Poulding S, Alexander R, Clark JA, Hadley MJ. The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing. Journal of Systems and Software. 2015 May 1;103:296-310. https://doi.org/10.1016/j.jss.2014.11.042

Author

Poulding, S. ; Alexander, R. ; Clark, J.A. ; Hadley, M.J. / The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing. In: Journal of Systems and Software. 2015 ; Vol. 103. pp. 296-310.

Bibtex - Download

@article{d99e88fcf87c4d1b8546ee7d42ba50cd,
title = "The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing",
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{\textquoteright}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.",
keywords = "Grammar-based testing",
author = "S. Poulding and R. Alexander and J.A. Clark and M.J. Hadley",
note = "This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy.",
year = "2015",
month = may,
day = "1",
doi = "10.1016/j.jss.2014.11.042",
language = "English",
volume = "103",
pages = "296--310",
journal = "Journal of Systems and Software",
issn = "0164-1212",
publisher = "Elsevier",

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RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing

AU - Poulding, S.

AU - Alexander, R.

AU - Clark, J.A.

AU - Hadley, M.J.

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

PY - 2015/5/1

Y1 - 2015/5/1

N2 - 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.

AB - 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.

KW - Grammar-based testing

U2 - 10.1016/j.jss.2014.11.042

DO - 10.1016/j.jss.2014.11.042

M3 - Article

VL - 103

SP - 296

EP - 310

JO - Journal of Systems and Software

JF - Journal of Systems and Software

SN - 0164-1212

ER -