Debugging tools should present execution history in time order to support better hypothesis generation about program behavior.
InProceedings of the 13th International Conference on Software Engineering (ICSE 2008)
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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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Tracers for debugging and program exploration
Debugging tools should present execution history in time order to support better hypothesis generation about program behavior.
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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.