A true positives theorem for a static race detector
- Submitting institution
-
Middlesex University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1086
- Type
- E - Conference contribution
- DOI
-
10.1145/3290370
- Title of conference / published proceedings
- Proceedings of the ACM on Programming Languages, Volume 3 Issue POPL
- First page
- 1
- Volume
- -
- Issue
- -
- ISSN
- 2475-1421
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2019
- URL
-
http://eprints.mdx.ac.uk/29747/
- 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)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- RacerD is a fast, compositional tool for automatically detecting data races in concurrency. RacerD’s analysis differs from previous work by reducing false positives over false negatives by not proving the absence of certain races, but proving their presence. This paper proves an important True Positives theorem in this context. This paper is significant because it exemplifies a way of studying program analysis tools designed for catching bugs rather than ensuring their absence. RacerD is included in Facebook’s static analysis tool Infer and used widely in industry.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -