Inferring Extended Finite State Machine Models from Software Executions
- Submitting institution
-
The University of Leicester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1398
- Type
- D - Journal article
- DOI
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10.1007/s10664-015-9367-7
- Title of journal
- Empirical Software Engineering: an international journal
- Article number
- 3
- First page
- 811
- Volume
- 21
- Issue
- 3
- ISSN
- 1382-3256
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
https://doi.org/10.1007/s10664-015-9367-7
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 44
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper in a major journal shows how to infer Extended FSMs (with data-constrained transitions) from program executions, a valuable tool for software testing and verification. This work has influenced multiple domains, e.g., intrusion detection (Khraisat et al., Cybersecurity, 2019) and industrial automation (Buzhinsky&Vyatkin, IEEE Trans Industrial Informatics, 2017). The openly-available framework has been used by U. Carlton and industrial partners for UAV systems testing and as baseline for evaluating other approaches (Eman&Miller, ACM TOSEM 2018). This widely-cited work contributed to several workpackages in the FP7 PROWESS project involving industrial collaborators, including Quviq (Hughes, CEO) and Erlang Solutions (Cesarini, Founder).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -