Researchers Develop Automated Method to Match Interview Transcripts with Software Requirements
Computer scientists have created an automated system using large language models to align interview transcripts with software requirements expressed as user stories. The approach introduces two metrics—requirements faithfulness and interview coverage—to measure how well derived requirements reflect stakeholder needs. This work addresses a significant challenge in software development where manually verifying that requirements accurately capture stakeholder intentions is time-consuming and error-prone.
Researchers at arXiv have formalized the task of automatically matching interview transcripts with software requirements represented as user stories, a process typically done manually. They propose two heuristic metrics: requirements faithfulness (the proportion of user stories supported by interview content) and interview coverage (the proportion of interview content reflected in at least one story). Testing their approach on four datasets using large language models and embedding models, they achieved a macro-F1 score of 0.86 on manually labeled chunk-story pairs. The embedding models can also serve as efficiency filters to improve scalability. The authors suggest this framework and automated matching techniques provide foundational components for emerging applications such as requirements tracing and automated requirements generation from conversational data.
What's missing
The paper does not specify which four datasets were used for evaluation, their characteristics, or how representative they are of real-world software elicitation scenarios. Additionally, the limitations of the approach—such as performance on domain-specific terminology, handling of implicit requirements, or failure modes—are not detailed in the abstract.
What different sources said
- arXiv cs.CLCenter
Automated Alignment between Elicitation Interviews and Requirements
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