Compiler Fuzzing through Deep Learning
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 84676742
- Type
- E - Conference contribution
- DOI
-
10.1145/3213846.3213848
- Title of conference / published proceedings
- Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis
- First page
- 95
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2018
- 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
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3
- Research group(s)
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A - Computer Systems
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This output automated the process of testing compilers for errors by using a deep learned language model to generate test code. It won a distinguished paper award in ISSTA 2018, an ACM SIGSOFT conference with 23% acceptance rate. The fuzzing toolchain is currently used by Codeplay, one of the leading companies for heterogeneous development tools, to find errors in their own compilers (contact: VP Product Engineering).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -