CCGMAS: A Multi-Agent Framework for Cross-Platform Go Code Generation via Requirement-Centered Semantic Modeling and Feedback-Driven Verification

Abstract

Cross-platform code generation is a critical challenge in modern software engineering, particularly for systems targeting heterogeneous instruction set architectures and operating systems. Although large language models (LLMs) have demonstrated strong capabilities in code generation, existing approaches often suffer from implicit semantic modeling, insufficient handling of platform-specific constraints, and lack of reliable validation mechanisms, leading to unstable and non-compilable outputs in real-world scenarios. To address these limitations, this paper proposes CCGMAS, a multi-agent framework for cross-platform Go code generation. The framework consists of four collaborative agents: a Platform Residue Analysis Agent that extracts platform-dependent semantics and constructs a structured platform residue profile, a Requirement Generation Agent that transforms multi-source code context into a requirement-centered semantic representation with iterative quality optimization, a Code Generation Agent that produces target-platform implementations through multi-candidate synthesis, and a Verification Agent that performs progressive validation and provides structured feedback. By introducing requirement documents as an intermediate semantic layer and incorporating platform residue modeling, CCGMAS enables more explicit semantic alignment across platforms. Furthermore, a feedback-driven refinement loop is designed to iteratively correct errors at different stages, improving both functional correctness and platform compatibility. Experiments on CCGBench demonstrate that CCGMAS reduces the platform residue rate by up to 62.2% in the best case and improves the cross-platform build pass rate by up to 45.0% compared with direct translation baselines.