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Title: Behaviourally Adequate Software Testing
Authors: Fraser, Gordon
Walkinshaw, Neil
First Published: Apr-2012
Presented at: Software Testing, Verification and Validation (ICST), 2012 IEEE Fifth International Conference on, 17-21 April 2012, Montreal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Software Testing, Verification and Validation (ICST), 2012 IEEE Fifth International Conference on, Proceedings of, pp. 300-309
Abstract: Identifying a finite test set that adequately captures the essential behaviour of a program such that all faults are identified is a well-established problem. Traditional adequacy metrics can be impractical, and may be misleading even if they are satisfied. One intuitive notion of adequacy, which has been discussed in theoretical terms over the past three decades, is the idea of behavioural coverage; if it is possible to infer an accurate model of a system from its test executions, then the test set must be adequate. Despite its intuitive basis, it has remained almost entirely in the theoretical domain because inferred models have been expected to be exact (generally an infeasible task), and have not allowed for any pragmatic interim measures of adequacy to guide test set generation. In this work we present a new test generation technique that is founded on behavioural adequacy, which combines a model evaluation framework from the domain of statistical learning theory with search-based white-box test generation strategies. Experiments with our BESTEST prototype indicate that such test sets not only come with a statistically valid measurement of adequacy, but also detect significantly more defects.
DOI Link: 10.1109/ICST.2012.110
ISBN: 978-1-4577-1906-6
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: © 2012 IEEE. Deposited with reference to the publisher's archiving policy available on the SHERPA/RoMEO website. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

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