Clotho ranks LLM test inputs by failure likelihood using pre-generation hidden states and GMMs, achieving 0.716 ROC-AUC after labeling 5.4% of inputs on average across eight tasks and three models, with transfer to proprietary models.
Bug In the Code Stack: Can LLMs Find Bugs in Large Python Code Stacks
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Locally deployed LLMs achieve 43-45% accuracy on Python bug detection but frequently produce only partial identifications of problematic code regions.
RAG-enhanced LLMs show generally positive effects on automated test generation and code inspection by supplying supplementary context that reduces hallucinations.
citing papers explorer
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Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs
Clotho ranks LLM test inputs by failure likelihood using pre-generation hidden states and GMMs, achieving 0.716 ROC-AUC after labeling 5.4% of inputs on average across eight tasks and three models, with transfer to proprietary models.
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An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code
Locally deployed LLMs achieve 43-45% accuracy on Python bug detection but frequently produce only partial identifications of problematic code regions.
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Enhancing Large Language Models with Retrieval Augmented Generation for Software Testing and Inspection Automation
RAG-enhanced LLMs show generally positive effects on automated test generation and code inspection by supplying supplementary context that reduces hallucinations.