Researchers Develop Automated Method to Match Interview Transcripts with Software Requirements
Researchers have formalized a framework for automatically aligning stakeholder interview transcripts with software requirements expressed as user stories, achieving 0.86 macro-F1 using large language models. The work introduces two metrics—requirements faithfulness and interview coverage—to evaluate how well derived requirements reflect stakeholder needs. The approach could reduce the manual effort currently required to verify that software requirements accurately capture what stakeholders actually said.
A paper submitted to arXiv introduces a formal framework for matching conversational elicitation interview transcripts to collections of software requirements written as user stories. The authors define two heuristic metrics: requirements faithfulness, measuring the proportion of user stories supported by the transcript, and interview coverage, measuring the proportion of the transcript supported by at least one story. Experiments conducted across four datasets demonstrate that an LLM-based solution achieves 0.86 macro-F1 on manually labeled chunk-story pairs, indicating strong automated alignment performance. The study also shows that embedding models can serve as blockers—filtering candidate pairs before LLM evaluation—to improve scalability. The authors position this work as foundational for downstream tasks such as tracing requirements back to interview sources and automatically generating requirements from conversational data.
What's missing
It is unclear how the approach performs when interview transcripts are noisy or when requirements are expressed in formats other than user stories. The study acknowledges this as early-stage work but does not discuss computational cost or latency of the LLM-based pipeline in production settings.
What different sources said
- arXiv cs.CLCenter
Automated Alignment between Elicitation Interviews and Requirements
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