使用大语言模型进行语义增强的架构恢复结果自动优化
Semantic-Enhanced Automatic Refinement of Architecture Recovery Results Using LLMs
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中文总结 AI 辅助
研究旨在简化架构恢复流程，提出Semref框架结合大语言模型与依赖分析自动优化架构，经在9个项目和10个工具上测试，采用5个指标评估，结果表明该框架提高了恢复架构的准确性。
中文摘要 AI 辅助
理解软件架构对大型软件系统的维护和管理至关重要，但设计架构与实现架构间常存在差异，提取架构既耗时又易出错。虽有自动架构恢复技术，但准确性有限。为简化流程，我们引入Semref框架，结合大语言模型与依赖分析，自动优化现有工具恢复的架构。通过利用大语言模型的语义理解能力并整合结构依赖，提高了恢复架构的准确性和可理解性。我们在9个有公开真实架构的项目和10个先进架构恢复工具上进行测试，采用5个常用指标评估，结果显示Semref在各项指标上提高了准确性，归一化增益范围为17.72%至43.35%。
英文摘要
Understanding the architecture is crucial for effectively maintaining and managing large software systems. However, discrepancies often exist between the designed and implemented architectures, which can pose significant risks. To identify these discrepancies, architects need to extract the architecture from the system implementation, which is both time-consuming and error-prone. To simplify this procedure, many automatic architecture recovery techniques have been developed. Yet, their accuracy is often limited. Architects must still invest significant effort in refining recovery results to ensure they accurately reflect the implemented architecture. To reduce such manual effort, we introduce Semref, a framework that combines LLMs with dependency analysis to automatically refine architectures recovered by existing architecture recovery tools. By leveraging the LLM's semantic understanding capabilities and integrating structural dependencies, Semref enhances both the accuracy and the comprehension of recovered architectures. To evaluate Semref, we tested on 9 projects with published ground-truth architectures and 10 state-of-the-art architecture recovery tools. 5 commonly used metrics are adopted to evaluate the effectiveness of Semref. The results show that Semref improves accuracy across various metrics, with normalized gains ranges from 17.72\% to 43.35\%. Specifically, for MoJoFM and $a2a_{adj}$ metrics, Semref achieves relative improvements of 118.57\% and 100.41\%, respectively.
