Skalpel: A constraint-based type error slicer for standard ML
- Submitting institution
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Heriot-Watt University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 15185065
- Type
- D - Journal article
- DOI
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10.1016/j.jsc.2016.07.013
- Title of journal
- Journal of Symbolic Computation
- Article number
- -
- First page
- 164
- Volume
- 80
- Issue
- Part 1
- ISSN
- 0747-7171
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2016
- 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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-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Skalpel is the only tool finding MTESs for a full (unrestricted) programming language, because its constraint/environments are linear (not exponential) in program size.
MTESs have become vital for other type error approaches: Seidel et al. (OOPSLA 2017) Sakkas et al. (PLDI 2020)
I led a long-term project to develop the necessary techniques for constraints-based slicing of whole causes of type errors to obtain minimal type-error slices. I supervised work on this project by a research assistant, two PhD students.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
