ArchAgent: Scalable Legacy Software Architecture Recovery with LLMs

• URL: http://arxiv.org/abs/2601.13007v1
• Date: Mon, 19 Jan 2026 12:39:05 GMT
• Title: ArchAgent: Scalable Legacy Software Architecture Recovery with LLMs
• Authors: Rusheng Pan, Bingcheng Mao, Tianyi Ma, Zhenhua Ling,
• Abstract summary: ArchAgent is a scalable agent-based framework that combines static analysis, adaptive code segmentation, and LLM-powered synthesis.<n>It reconstructs multiview, business-aligned architectures from cross-repositorys.<n>ArchAgent introduces scalable diagram generation with contextual pruning and integrates cross-repository data to identify business-critical modules.
• Score: 44.137226823695066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recovering accurate architecture from large-scale legacy software is hindered by architectural drift, missing relations, and the limited context of Large Language Models (LLMs). We present ArchAgent, a scalable agent-based framework that combines static analysis, adaptive code segmentation, and LLM-powered synthesis to reconstruct multiview, business-aligned architectures from cross-repository codebases. ArchAgent introduces scalable diagram generation with contextual pruning and integrates cross-repository data to identify business-critical modules. Evaluations of typical large-scale GitHub projects show significant improvements over existing benchmarks. An ablation study confirms that dependency context improves the accuracy of generated architectures of production-level repositories, and a real-world case study demonstrates effective recovery of critical business logics from legacy projects. The dataset is available at https://github.com/panrusheng/arch-eval-benchmark.

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