Diagnosability under Weak Fairness
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 219507-80856-1292
- Type
- D - Journal article
- DOI
-
10.1145/2832910
- Title of journal
- ACM Transactions on Embedded Computing Systems
- Article number
- 69
- First page
- -
- Volume
- 14
- Issue
- 4
- ISSN
- 1539-9087
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2015
- URL
-
http://dx.doi.org/10.1145/2832910
- 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
-
3
- Research group(s)
-
A - Advanced Model-Based Engineering and Reasoning (AMBER)
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Nominated for the best paper award and selected for journal special issue as one of best papers of ACSD’14 conference. After identifying a flaw in the existing literature, it solved the difficult problem of diagnosability under weak fairness by reducing it to model checking that is well studied and supported by many software tools. The paper is a result of international collaboration with ENS Cachan, CNRS, and INRIA, and an important outcome of EPSRC projects EP/K001698/1, EP/L025507/1, and French National Research Agency project ANR-2010-BLAN-0317.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
