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Learning Units-of-Measure from Scientific Code

Danish, Matthew, Allamanis, Miltiadis, Brockschmidt, Marc, Rice, Andrew, Orchard, Dominic A. (2019) Learning Units-of-Measure from Scientific Code. In: 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science). Proceedings: 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science 2019). . pp. 43-46. IEEE ISBN 978-1-72812-277-9. E-ISBN 978-1-72812-276-2. (doi:10.1109/SE4Science.2019.00013) (KAR id:79924)

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https://doi.org/10.1109/SE4Science.2019.00013

Abstract

CamFort is our multi-purpose tool for lightweight analysis and verification of scientific Fortran code. One core feature provides units-of-measure verification (dimensional analysis) of programs, where users partially annotate programs with units-of-measure from which our tool checks consistency and infers any missing specifications. However, many users find it onerous to provide units-of-measure information for existing code, even in part. We have noted however that there are often many common patterns and clues about the intended units-of-measure contained within variable names, comments, and surrounding code context. In this work-in-progress paper, we describe how we are adapting our approach, leveraging machine learning techniques to reconstruct units-of-measure information automatically thus saving programmer effort and increasing the likelihood of adoption.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/SE4Science.2019.00013
Divisions: Faculties > Sciences > School of Computing
Depositing User: Dominic Orchard
Date Deposited: 03 Feb 2020 10:05 UTC
Last Modified: 19 Feb 2020 16:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79924 (The current URI for this page, for reference purposes)
Orchard, Dominic A.: https://orcid.org/0000-0002-7058-7842
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