DEFault++ delivers automated hierarchical fault detection, categorization into 12 transformer-specific types, and root-cause diagnosis among 45 mechanisms on a new benchmark of 3,739 mutated instances, with AUROC >0.96 and Macro-F1 0.85, plus improved developer repair accuracy in a user study.
A multi-agent approach to fault localization via graph-based retrieval and reflexion.arXiv preprint arXiv:2409.13642
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Introduces EPC-AW to mitigate epistemic miscalibration in LLM multi-agent planning via consistency-based selection and refinement, reporting 9.75% average success improvement.
SieveFL combines vector retrieval and JaCoCo runtime pruning to cut LLM token use by 49% while achieving 41.8% Top-1 accuracy on 395 Defects4J bugs, outperforming AgentFL.
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.
citing papers explorer
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DEFault++: Automated Fault Detection, Categorization, and Diagnosis for Transformer Architectures
DEFault++ delivers automated hierarchical fault detection, categorization into 12 transformer-specific types, and root-cause diagnosis among 45 mechanisms on a new benchmark of 3,739 mutated instances, with AUROC >0.96 and Macro-F1 0.85, plus improved developer repair accuracy in a user study.
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When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
Introduces EPC-AW to mitigate epistemic miscalibration in LLM multi-agent planning via consistency-based selection and refinement, reporting 9.75% average success improvement.
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SieveFL: Hierarchical Runtime-Aware Pruning for Scalable LLM-Based Fault Localization
SieveFL combines vector retrieval and JaCoCo runtime pruning to cut LLM token use by 49% while achieving 41.8% Top-1 accuracy on 395 Defects4J bugs, outperforming AgentFL.
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Towards Explorative IRBL: Combining Semantic Retrieval with LLM-driven Iterative Code Exploration
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.