研究测试了四种大模型从代码提交中提取架构设计决策的能力，发现虽有一定效果但仍有改进空间。
研究使用Gemini 3 Pro、DeepSeek R1、Kimi K2和Qwen3四种大模型，从30个开源项目的开发者提交中提取架构设计决策(ADD)。所有模型的BERT-F1分数均超过0.81，少样本提示使Gemini的BERT-F1从0.828提升至0.847。但生成的ADD通常过长、过于关注实现细节，且缺乏决策背后的理由。
Can LLMs Extract Architectural Design Decisions from Source Code Commits? - A Preliminary Exploratory Study
Context: Architectural Design Decisions (ADDs) capture the rationale behind the structure and evolution of software systems but are rarely documented explicitly, and are often hidden inside source code commits. Recovering them is important for Architectural Knowledge Management (AKM). Problem: Extracting ADDs from commits is challenging due to their implicit and unstructured nature. Large Language Models (LLMs) have shown strong capabilities in understanding code and text, yet their effectiveness for this task remains underexplored. Study: We present a preliminary study using four LLMs (Gemini 3 Pro, DeepSeek R1, Kimi K2, Qwen3) with zeroshot and fewshot prompting on 30 developer-written ADDs from open-source projects. We score outputs with ROUGE-L, BLEU, METEOR, and BERTScore, and one author manually reviews the Gemini outputs. Results: All models reach a BERT-F1 above 0.81, and fewshot prompting improves alignment (Gemini BERT-F1: 0.828 to 0.847). However, the generated ADDs are often too long, implementation-focused, and miss the rationale behind the decision. This highlights opportunities for architecture-aware LLM systems and automated AKM.