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The Optimisation of Stochastic Grammars to Enable Cost-Effective Probabilistic Structural Testing
Research output: Contribution to conference › Paper › peer-review
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Author(s)
Department/unit(s)
Conference
| Conference | GECCO 2013 |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Conference date(s) | 6/07/13 → 10/07/13 |
Publication details
| Date | Published - Jul 2013 |
|---|---|
| Number of pages | 8 |
| Original language | English |
Abstract
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.
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.
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???prize-relations???
Best Paper Prize at GECCO Search Based Software Engineering (SBSE) 2013
Prize: Prize (including medals and awards)
Best Paper SBSE Winner
Prize: Prize (including medals and awards)
Best Paper SBSE winner
Prize: Prize (including medals and awards)
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