Can LLMs Extract Architectural Design Decisions from Source Code Commits? - A Preliminary Exploratory Study

The study investigates whether large language models can extract Architectural Design Decisions (ADDs) from source code commits. Using four LLMs (Gemini 3 Pro, DeepSeek R1, Kimi K2, Qwen3) with zero‑shot and few‑shot prompting on 30 developer‑written ADDs, the authors evaluate outputs with ROUGE‑L, BLEU, METEOR, and BERTScore. Results show all models achieve a BERT‑F1 above 0.81, with few‑shot prompting slightly improving alignment, but the generated ADDs tend to be overly long, implementation‑focused, and lack the rationale behind the decisions.

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