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Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions. (arXiv:2203.03771v1 [cs.LG])
March 9, 2022, 2:11 a.m. | David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow
cs.LG updates on arXiv.org arxiv.org
The execution behavior of a program often depends on external resources, such
as program inputs or file contents, and so cannot be run in isolation.
Nevertheless, software developers benefit from fast iteration loops where
automated tools identify errors as early as possible, even before programs can
be compiled and run. This presents an interesting machine learning challenge:
can we predict runtime errors in a "static" setting, where program execution is
not possible? Here, we introduce a real-world dataset and task …
More from arxiv.org / cs.LG updates on arXiv.org
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