A fully static, LLM-driven pipeline estimates the execution trace from a single failure log, prunes the test code, and ranks faulty locations at function, block, and line levels, tested on 785 industrial Python test cases.
Towards optimizing the costs of llm usage
4 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
D2D adaptively prioritizes informative attribute queries and times recommendations in conversational search, yielding 22-30% higher target accuracy and shorter conversations than baselines in simulations.
CAMI frames multi-index construction for semantic retrieval as a budgeted multi-objective portfolio problem and uses agent-guided search plus confidence-aware pruning to find high-recall configurations with reduced evaluation cost.
Quantization and local inference reduce LLM energy consumption and emissions by up to 45% in a presented case study.
citing papers explorer
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Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models
A fully static, LLM-driven pipeline estimates the execution trace from a single failure log, prunes the test code, and ranks faulty locations at function, block, and line levels, tested on 785 industrial Python test cases.
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Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants
D2D adaptively prioritizes informative attribute queries and times recommendations in conversational search, yielding 22-30% higher target accuracy and shorter conversations than baselines in simulations.
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CAMI: Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval
CAMI frames multi-index construction for semantic retrieval as a budgeted multi-objective portfolio problem and uses agent-guided search plus confidence-aware pruning to find high-recall configurations with reduced evaluation cost.
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Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights
Quantization and local inference reduce LLM energy consumption and emissions by up to 45% in a presented case study.