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Title: Inferring Extended Finite State Machine Models from Software Executions
Authors: Walkinshaw, Neil
Taylor, R.
Derrick, J.
First Published: 17-Mar-2015
Publisher: Springer Verlag (Germany)
Citation: Journal of Empirical Software Engineering
Abstract: The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these techniques can be non-deterministic, the inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM inference technique that addresses the problems of inflexibility and non-determinism. It also adapts an experimental technique from the field of Machine Learning to evaluate EFSM inference techniques, and applies it to three diverse software systems.
DOI Link: 10.1007/s10664-015-9367-7
ISSN: 1382-3256
eISSN: 1573-7616
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Archived with reference to SHERPA/RoMEO and publisher website. The final publication is available at Springer via
Appears in Collections:Published Articles, Dept. of Computer Science

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