Who's Who? LLM-assisted Software Traceability with Architecture Entity Recognition
This work addresses the time-consuming manual creation of software architecture models for traceability, making it more practical for software engineers, though it is incremental as it extends prior research.
The paper tackles automating software architecture traceability by using LLMs to extract architectural entities from documentation and source code, achieving F1 scores up to 0.86 for component name extraction and 0.81 for entity matching, comparable to state-of-the-art methods.
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.