On abstraction refinement for program analyses in Datalog
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9526
- Type
- E - Conference contribution
- DOI
-
10.1145/2594291.2594327
- Title of conference / published proceedings
- Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI '14
- First page
- 239
- Volume
- -
- Issue
- -
- ISSN
- 0362-1340
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
https://kar.kent.ac.uk/54177/
- 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
-
4
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Our work proposes a completely general method of applying CEGAR to any static analysis that is implemented in Datalog. This paper is significant because our theory is complemented by experiments that led to a step-change in performance. A DARPA project (AD1098764) used our implementation to find 100 bugs in open-source code. After publication, it was realised that our method is also applicable to the growing field of Horn-clause-based verification. It received a distinguished paper award.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
