一句话：对 DSAR 软件架构恢复在 ASML 17 个 Java 仓库中的工业定性评估，聚焦解释性制品的质量与实用性。
理解大型复杂软件架构困难且耗时，现有架构恢复方法常缺乏泛化性。本文对作者先前提出的演绎式软件架构恢复（DSAR）进行工业场景定性评估。作者借助大语言模型辅助原型，在 ASML 多个软件工程团队中开展深度判断研究，将 DSAR 应用于 17 个来自不同团队的 Java 仓库，考察恢复架构的内容质量、呈现质量及其对理解与维护真实系统的有用性。结果表明，DSAR 能支持架构理解、架构建模推理与团队沟通；研究还给出生成解释性制品的优势与局限的定性证据，并为实践者和研究者采用及进一步发展 DSAR 进行架构意义建构提供建议。
Understanding large, complex software architectures is difficult and time-consuming. Software architecture recovery aims to extract explanatory artefacts from code, but existing methods often lack generalisability. To address this, we previously proposed Deductive Software Architecture Recovery (DSAR) to extend current techniques. This paper presents a qualitative evaluation of DSAR in an industrial setting. Using a large language model-assisted prototype, we examined the recovered architecture's content quality, presentation quality, and usefulness for understanding and maintaining real-world systems. To ensure practical relevance, we conducted an in-depth judgement study with software engineering teams at ASML applying DSAR to 17 Java repositories across diverse teams. We contribute: an empirical assessment indicating that DSAR supports comprehension, architectural reasoning, and communication; qualitative evidence on the strengths and limitations of the generated explanatory artefacts; and recommendations for practitioners and researchers on adopting and further developing DSAR for architectural sense-making.
@inproceedings{RukmonoOBKC26,
title = {Evaluating Explanatory Artefacts of DSAR-Recovered Software Architectures from Industrial Codebases},
author = {Satrio Adi Rukmono and Lina Ochoa and Thijs Bressers and Jacob Krüger and Michel Chaudron},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805259}
}