发表机构
University of Lugano; Technical University of Munich; University of Augsburg（卢加诺大学; 慕尼黑工业大学; 奥格斯堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对COBOL代码翻译难题，提出SEDCoT框架。先利用大语言模型初译，再结合符号执行与大语言模型指导生成测试套件修复语义差异，最后用增量调试最小化失败测试，提升翻译性能与可读性。
AI 中文摘要
COBOL在银行、保险和政府基础设施中仍至关重要。由于技术过时、文档稀少和开发者退休，维护日益困难，需要将代码翻译成C等现代语言。传统基于规则的转编译器输出难以阅读和维护，通用大语言模型正确性欠佳。为此提出SEDCoT框架，经实验验证性能优于现有基线。
英文摘要
COBOL remains critical across banking, insurance, and government infrastructure. However, maintenance is increasingly challenging due to outdated technologies, sparse documentation, and developer retirement, necessitating code translation into modern languages like C. Traditional rule-based transcompilers yield outputs that are difficult to read and maintain, while general-purpose large language models (LLMs) achieve suboptimal correctness because COBOL is a low-resource language with distinct logic patterns. To bridge this gap, we propose SEDCoT, a novel COBOL-to-C translation framework. SEDCoT first leverages LLMs for initial translation, then combines symbolic execution with LLM guidance to generate test suites and iteratively repair semantic discrepancies. Finally, it integrates delta debugging to minimize failing tests into succinct counterexamples, accelerating automated code repair. Evaluating SEDCoT on a public COBOL-to-C dataset demonstrates that it outperforms state-of-the-art baselines by at least 12% while producing translations with substantially higher readability than rule-based alternatives.
