Walkinshaw, N., Taylor, R. and Derrick, J. (2015) Inferring extended finite state machine models from software executions. Empirical Software Engineering. ISSN 1382-3256
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2015 Springer Science+Business Media New York. This is an author produced version of a paper subsequently published in Empirical Software Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Reverse engineering; EFSMs; Dynamic analysis |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Jan 2016 16:04 |
Last Modified: | 08 Apr 2019 12:50 |
Published Version: | http://dx.doi.org/10.1007/s10664-015-9367-7 |
Status: | Published |
Publisher: | Springer Verlag |
Refereed: | Yes |
Identification Number: | 10.1007/s10664-015-9367-7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:92919 |