Inferring extended finite state machine models from software executions
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2523
- Type
- D - Journal article
- DOI
-
10.1007/s10664-015-9367-7
- Title of journal
- Empirical Software Engineering
- Article number
- -
- 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
-
-
- 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)
-
H - Testing
- Citation count
- 44
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper develops a new approach to infer Extended Finite State Machines. Its novelty lies in its flexibility; a wide range of off-the-shelf ML algorithms can be selected to infer data guards on the transitions. The research resulted in an openly-available tool that has been used to explore and evaluate numerous novel applications. Examples include inferring plant models for industrial systems (IEEE Transactions on Industrial Systems - doi.org/10.1109/TII.2017.2670146), inferring UAV controllers (ICPC - doi.org/10.1109/ICPC.2019.00020), and evaluating new approaches to infer probabilistic EFSMs (TOSEM - doi.org/10.1145/3196883).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -