Empirical Software Engineering

, Volume 21, Issue 3, pp 811–853

Inferring extended finite state machine models from software executions

Article

DOI: 10.1007/s10664-015-9367-7

Cite this article as:
Walkinshaw, N., Taylor, R. & Derrick, J. Empir Software Eng (2016) 21: 811. doi:10.1007/s10664-015-9367-7

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.

Keywords

Reverse engineering EFSMs Dynamic analysis 

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of LeicesterLeicesterUK
  2. 2.Department of Computer ScienceThe University of SheffieldSheffieldUK

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