ASNER: Adaptive Semantic Neighborhoods and Evidence-Grounded Refinement for Software Architecture Recovery

Automated software architecture recovery is important for software engineering, especially as large language model (LLM) agents increasingly perform repository-level tasks that require architectural context to understand and modify large codebases. Existing methods combine structural and semantic signals, but the resulting relations do not necessarily reflect architectural responsibility. Files that co-implement one component may appear dissimilar, while related files may merely cooperate across component boundaries. Fixed thresholds or Top-K neighborhoods also ignore repository scale and local similarity distributions, causing missing within-component links or noisy cross-component edges. These errors propagate to community detection, while unconstrained LLM refinement may alter correct assignments without sufficient evidence. Our key insight is that accurate recovery requires responsibility-aware semantic relations that adapt to each repository, followed by evidence-grounded verification of uncertain boundaries. We therefore propose ASNER (Adaptive Semantic Neighborhoods and Evidence-Grounded Refinement for Software Architecture Recovery). ASNER uses an LLM to derive structured file responsibilities, constructs adaptive semantic neighborhoods, and augments a static dependency graph with selected semantic relations and weak directory cues. Community detection recovers the component backbone, after which a Responsibility-Ownership-Aware (ROA) Agent revisits uncertain boundary files using project-level responsibilities and traceable code evidence, changing assignments only when supported. We evaluate ASNER on 12 open-source projects spanning C, C++, Java, and Python. ASNER achieves the best macro-average on all five metrics on the common benchmark, improving ARI and c2c@66 over GAER-GAT by 19.5% and 32.9%, respectively. A downstream evaluation further shows that ASNER-guided LocAgent improves file-, module-, and function-level code localization. These results demonstrate the value of ASNER’s architectural context for repository-level software engineering tasks.