发表机构
Inria; CNRS; Univ. of Rennes; IRISA; INSA（法国国家信息与自动化研究所; 法国国家科学研究中心; 雷恩大学; 雷恩信息系统与随机系统研究所; 法国国立应用科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种智能体AI流水线，通过规范驱动生成与迭代退化生成真实COBOL程序，评估其结构复杂度与业务行为保持，并指出局限性及未来改进方向。
AI 中文摘要
COBOL仍然被广泛部署，但反映真实生产代码的代表性语料库却很少可用，这限制了对现代化方法进行严格基准测试。我们提出了一种系统性的智能体AI流水线，用于生成真实的COBOL程序，将规范驱动的生成与基于从真实生产代码中提取的模式和复杂度目标的迭代退化相结合。在这里，真实性被理解为对生产代码的结构保真度，由我们的指标捕获。我们评估退化是否在保持业务行为的同时达到目标复杂度水平，并考察该方法的局限性，涉及来自不同业务领域的三个程序。结果表明，该流水线能够可靠地生成语法有效的程序，并使其向真实的结构复杂度发展。然而，保持业务行为并非总能通过构造实现，且独立于业务逻辑来瞄准结构指标，可能会产生复杂度无法反映合理维护历史的程序。我们讨论了这些局限性，并概述了一种更现实的替代方案作为未来工作的方向，即沿着模拟的开发历史从头生成遗留程序。
英文摘要
COBOL remains widely deployed, yet representative corpora reflecting real production code are rarely available, limiting rigorous benchmarking of modernization approaches. We propose a systematic agentic AI pipeline for generating realistic COBOL programs, combining specification-driven generation with iterative degradation guided by patterns and complexity targets extracted from real production code. Here, realism is understood as structural fidelity to production code as captured by our metrics. We evaluate whether degradation reaches target complexity levels while preserving business behavior, and examine the limits of the approach, across three programs from distinct business domains. Results show the pipeline reliably produces syntactically valid programs and moves them toward realistic structural complexity. However, preserving business behavior is not always achieved by construction, and targeting structural metrics independently of business logic risks producing programs whose complexity does not reflect a plausible maintenance history. We discuss these limitations and outline a more realistic alternative as a direction for future work, generating legacy programs from scratch along a simulated development history.
Journal refThe 2nd International Workshop on AI for Software Modernization (AISM 2026) co-located with Automated Software Engineering Conference, Oct 2026, Munich (Allemagne), Germany
