Learning the Language of Software Errors
- Submitting institution
-
King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 127908058
- Type
- D - Journal article
- DOI
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10.1613/jair.1.11798
- Title of journal
- Journal of Artificial Intelligence Research
- Article number
- -
- First page
- 881
- Volume
- 67
- Issue
- -
- ISSN
- 1076-9757
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2020
- 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
-
3
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces a completely new way of analysing software, by learning the language of its behaviours automatically and presenting its logic graphically in an abstract way as a finite automaton. This technique can be naturally applied to the problem of concise representation of all errors in a given software or to the software exploration problem which requires a new developer to familiarise themselves with a large existing codebase. All results are formally proven, algorithms implemented and evaluated on standard benchmarks and real-life examples, showing minimal overheads. This work was funded by Chockler’s Google Faculty Award and presented to Google.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -