Software Architecture Recovery Augmented With Semantics

The architecture of software systems evolves along with their upgrades and maintenance, inevitably creating a gap between the defact architecture and the designed one. To perceive and fix the discrepancy, clustering-based architecture recovery methods have been developed to re-engineer the real-time system architecture from the code implementation. However, existing solutions still face several limitations. They underutilize both code-level and architecture-level semantics underlying the source code. Moreover, they overlook implicit structural dependencies that complement explicit ones to reflect code interactions. To address these challenges, we propose SemArc, an architecture recovery method that utilizes large language models to comprehend both implementation-level and architecture-level semantics, supported by well-established canonical architectural patterns as a knowledge base. SemArc also incorporates both implicit and explicit dependencies to complete the system behavior representations. Additionally, SemArc introduces a component-as-anchor guided clustering algorithm to improve the clustering process. We evaluated SemArc on 15 software systems written in C/C++, Java, and Python, using five different metrics. The results demonstrate that SemArc outperforms seven baseline methods by an average of 32 percentage points. We also examined how three factors—code semantics, architectural semantics, and implicit dependencies—as well as different levels of architectural semantic descriptions, influence recovery accuracy. A case study on the Bash project indicates that SemArc has the potential to yield even more precise recovery results than those labeled by humans.