GADR: Gathering Architecture Decision Records from Meeting Transcriptions

Abstract:Existing LLM-based approaches to Architecture Decision Record (ADR) generation share a critical and largely unexamined assumption: that input is already reasonably structured. In practice, architectural decisions emerge from informal, noisy meetings where choices are implicit, fragmented, and entangled with off-topic dialogue, precisely the conditions under which single-pass prompting degrades. This paper presents GADR, a multi-agent, self-correcting workflow that extracts architectural decisions from raw meeting transcriptions and generates Nygard-formatted ADR drafts. A feasibility study comprising five real project meeting transcripts, expert review by four senior architects, and evaluation by fifteen students provides initial evidence that the agentic workflow captures most expert-identified decisions and produces drafts participants found clear and useful, outperforming zero-shot and few-shot baselines in stability and structural adherence. The study also addresses the underexplored trade-off of RAG-based enrichment improving ADR depth while simultaneously risking transcript-unfaithful content, raising open questions about traceability in automated architectural documentation that we believe is worth the community's attention.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2608.17694 [cs.SE]

(or arXiv:2608.17694v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2608.17694 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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