TraceLLM uses prompt engineering and label-aware demonstration selection with eight LLMs on four benchmark datasets to achieve state-of-the-art F2 scores for requirements traceability, outperforming IR baselines and prior LLM methods.
Adams Re-Trace: Traceability Recovery in Software Artifacts
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TraceLLM: Leveraging Large Language Models with Prompt Engineering for Enhanced Requirements Traceability
TraceLLM uses prompt engineering and label-aware demonstration selection with eight LLMs on four benchmark datasets to achieve state-of-the-art F2 scores for requirements traceability, outperforming IR baselines and prior LLM methods.