Inferring extended finite state machine models from software executions

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 behav...

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Bibliographic Details
Published in:Empirical software engineering : an international journal Vol. 21; no. 3; pp. 811 - 853
Main Authors: Walkinshaw, Neil, Taylor, Ramsay, Derrick, John
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2016
Springer Nature B.V
Subjects:
Algorithms
Compilers
Computer programs
Computer Science
Finite state machines
Inference
Interpreters
Invariants
Machine learning
Maintenance
Program verification (computers)
Programming Languages
Software
Software Engineering/Programming and Operating Systems
Tasks
Reverse engineering
EFSMs
Dynamic analysis
ISSN:1382-3256, 1573-7616
Online Access:Get full text
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Summary: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.
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ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-015-9367-7