A methodology mines 62,965 real Python syntax errors and fixes from Stack Overflow posts, releases the dataset publicly, and shows these errors differ from student or randomly mutated ones.
Syntax errors just aren’t natural: Improving error reporting with language models,
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DeepFWI is a multi-modal LSTM model with cross-attention that identifies bug-sensitive warnings at warning granularity, reaching 67.06% F1 on a 280k-warning dataset and surfacing 25 confirmed bugs in four open-source projects.
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Syntax and Stack Overflow: A methodology for extracting a corpus of syntax errors and fixes
A methodology mines 62,965 real Python syntax errors and fixes from Stack Overflow posts, releases the dataset publicly, and shows these errors differ from student or randomly mutated ones.
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DeepFWI: Identifying Bug-Sensitive Warnings with Multi-Modal Code-Warning Semantics
DeepFWI is a multi-modal LSTM model with cross-attention that identifies bug-sensitive warnings at warning granularity, reaching 67.06% F1 on a 280k-warning dataset and surfacing 25 confirmed bugs in four open-source projects.