DEFault++ applies hierarchical learning with a Fault Propagation Graph to detect, localize, and diagnose faults in transformers, improving F1 to 0.826-0.909 and developer repair accuracy from 57.1% to 83.3% on a new benchmark of 5,556 mutation-tested runs.
Where’s the bug? atten- tion probing for scalable fault localization.arXiv preprint arXiv:2502.13966
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FLARE combines a diagnostic model for line-level bug localization with top-k candidate search to improve LLM code refinement, reporting gains of 1.72-7.42% on LiveCodeBench and BigCodeBench.
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Hierarchical Fault Detection and Diagnosis for Transformer Architectures
DEFault++ applies hierarchical learning with a Fault Propagation Graph to detect, localize, and diagnose faults in transformers, improving F1 to 0.826-0.909 and developer repair accuracy from 57.1% to 83.3% on a new benchmark of 5,556 mutation-tested runs.
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FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement
FLARE combines a diagnostic model for line-level bug localization with top-k candidate search to improve LLM code refinement, reporting gains of 1.72-7.42% on LiveCodeBench and BigCodeBench.