Integrating Provenance Capture and UML with UML2PROV: Principles and Experience
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 124951458
- Type
- D - Journal article
- DOI
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10.1109/TSE.2020.2977016
- Title of journal
- IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
- Article number
- -
- First page
- 0
- Volume
- 0
- Issue
- 0
- ISSN
- 0098-5589
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2020
- URL
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- Supplementary information
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- Request cross-referral to
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- 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
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The originality of the UML2PROV methodology is to establish that UML designs can be annotated with modest human effort to allow applications to be automatically extended with provenance generation capabilities. UML2PROV significance lies in the generated provenance that allows applications behaviour to be explained. Empirical evaluation by means of the GelJ bioinformatics tool establishes a trade-off between human effort and provenance granularity. Supplementary material https://zenodo.org/record/3701784 includes a collection of 17 reusable patterns of provenance and associated UML designs. UML2PROV is described in the January 2019 IEEE IoT NewsLetter as a methodology for provenance-aware software coding to support explainable AI https://iot.ieee.org/newsletter/january-2019/explainable-ai-and-other-questions-where-provenance-matters.
- Author contribution statement
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- Non-English
- No
- English abstract
- -