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This paper presents a lightweight process to guide error report authoring. We take the perspective that error reports are really classifiers of program information. They should therefore be subjected to the same measures as other classifiers (e.g., precision and recall). We formalize this perspective as a process for assessing error reports, describe our application of this process to an actual programming language, and present a preliminary study on the utility of the resulting error reports.
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A robust error estimator and a residual-free error indicator for reduced basis methods
- Award ID(s):
- 1719698
- PAR ID:
- 10105995
- Date Published:
- Journal Name:
- Computers & Mathematics with Applications
- Volume:
- 77
- Issue:
- 7
- ISSN:
- 0898-1221
- Page Range / eLocation ID:
- 1963 to 1979
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.camwa.2018.11.032
