谁是谁？基于LLM的软件可追溯性与架构实体识别
Who's Who? LLM-assisted Software Traceability with Architecture Entity Recognition
AI总结：
本文提出ArTEMiS方法，通过语义推理实现架构实体匹配，结合ExArch自动构建SAM，提升软件架构与代码的可追溯性。
摘要：
识别文本 artifacts 中的架构相关实体对软件架构文档（SAD）与源代码之间可追溯性链接恢复（TLR）至关重要。虽然软件架构模型（SAMs）可以弥合这些 artifacts 之间的语义鸿沟，但其手动创建过程耗时。LLMs 提供了新的能力，能够从 SAD 和源代码中提取架构实体以自动构建 SAMs 或建立直接的可追溯链接。本文扩展了我们在 ICSA 2025 的论文 [19]，该论文介绍了用于基于 LLM 的架构组件名称提取的 Extracting Architecture（ExArch）。扩展贡献了新的 Architecture Traceability with Entity Matching via Semantic inference（ArTEMiS）方法，包括扩展的评估、额外的 LLMs、配置、修订的基准和两种方法的综合评估。具体而言，本文提出了以下方法：ExArch 从 SAD 和源代码中提取组件名称作为简单的 SAMs，以消除手动创建 SAMs 的需求，而 ArTEMiS 在文档中识别架构实体并将其与（手动或自动创建的）SAM 实体匹配。我们的评估对比了最先进的方法 SWATTR、TransArC 和 ArDoCode。TransArC 实现了强劲的性能（F1: 0.87），但需要手动创建 SAMs；ExArch 实现了相当的结果（F1: 0.86），仅使用 SAD 和代码。ArTEMiS 与传统的基于启发式的 SWATTR（F1: 0.81）相当，并且在与 TransArC 集成时可以成功替代它。ArTEMiS 和 ExArch 的结合优于 ArDoCode，即没有手动 SAMs 的最佳基线。我们的结果表明，LLMs 可以有效识别文本 artifacts 中的架构实体，实现自动 SAM 生成和 TLR，使架构-代码可追溯性更加实用和可访问。
英文摘要：
Identifying architecturally relevant entities in textual artifacts is crucial for Traceability Link Recovery (TLR) between Software Architecture Documentation (SAD) and source code. While Software Architecture Models (SAMs) can bridge the semantic gap between these artifacts, their manual creation is time-consuming. LLMs offer new capabilities for extracting architectural entities from SAD and source code to construct SAMs automatically or establish direct trace links. This paper extends our ICSA 2025 paper [19], which introduced Extracting Architecture (ExArch) for LLM-based architecture component name extraction. The extension contributes the novel Architecture Traceability with Entity Matching via Semantic inference (ArTEMiS) approach, an extended evaluation with additional LLMs, configurations, a revised benchmark, and a combined evaluation of both approaches. Specifically, this paper presents the following approaches: ExArch extracts component names as simple SAMs from SAD and source code to eliminate the need for manual SAM creation, while ArTEMiS identifies architectural entities in documentation and matches them with (manually or automatically generated) SAM entities. Our evaluation compares against state-of-the-art approaches SWATTR, TransArC and ArDoCode. TransArC achieves strong performance (F1: 0.87) but requires manually created SAMs; ExArch achieves comparable results (F1: 0.86) using only SAD and code. ArTEMiS is on par with the traditional heuristic-based SWATTR (F1: 0.81) and can successfully replace it when integrated with TransArC. The combination of ArTEMiS and ExArch outperforms ArDoCode, the best baseline without manual SAMs. Our results demonstrate that LLMs can effectively identify architectural entities in textual artifacts, enabling automated SAM generation and TLR, making architecture-code traceability more practical and accessible.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
