The Optimisation of Stochastic Grammars to Enable Cost-Effective Probabilistic Structural Testing
Research output: Contribution to conference › Paper › peer-review
Conference | GECCO 2013 |
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Country/Territory | Netherlands |
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City | Amsterdam |
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Conference date(s) | 6/07/13 → 10/07/13 |
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Date | Published - Jul 2013 |
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Number of pages | 8 |
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Original language | English |
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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.
Project: Research project (funded) › Research
Project: Research project (funded) › Research
Project: Research project (funded) › Research
Prize: Prize (including medals and awards)
Prize: Prize (including medals and awards)
Prize: Prize (including medals and awards)
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