Learning units-of-measure from scientific code
Proceedings - 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science, SE4Science 2019
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Danish, M., Allamanis, M., Brockschmidt, M., Rice, A., & Orchard, D. (2019). Learning units-of-measure from scientific code. Proceedings - 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science, SE4Science 2019, 43-46. https://doi.org/10.1109/SE4Science.2019.00013
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.
External DOI: https://doi.org/10.1109/SE4Science.2019.00013
This record's URL: https://www.repository.cam.ac.uk/handle/1810/290660