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 ...
| Authors | Amey Karan, Rudra Dhar, Mohamed Soliman et al. |
|---|---|
| Primary category | cs.SE |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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