Generalized observational slicing for tree-represented modelling languages
- Submitting institution
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University of East London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 21
- Type
- E - Conference contribution
- DOI
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10.1145/3106237.3106304
- Title of conference / published proceedings
- Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering
- First page
- 547
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- September
- Year of publication
- 2017
- 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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5
- Research group(s)
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2 - Enterprise Computing
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Model-driven software engineering makes complex systems easier to understand, but large software systems can still have large models.
This system generalizes observation-based slicing to identify the subset of the original model responsible for an observed failure, with no need of complex dependence analysis.
A study of nine real-world Simulink models from four different application domains demonstrates the effectiveness of our approach: for 9 out of 20 cases, the resulting model has fewer than 25% of the original model’s elements. It led to further publications [1,2]
[1] 10.1109/SCAM.2017.11 (Best Paper Award)
[2] 10.1007/s10664-018-9675-9
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -