Spec2COBOLRot: An Agentic-AI Degradation Loop for Realistic COBOL Corpus Generation

2026-09-21

AI Summary

为解决COBOL语料库缺乏问题，提出一种结合规范驱动生成与迭代退化的方法，生成结构上接近真实生产代码的COBOL程序。

Abstract

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.

Problem

• COBOL
• Realistic Corpus Generation
• Structural Fidelity
• Business Behavior
• Complexity Metrics

Innovation

• agentic AI pipeline
• specification-driven generation
• iterative degradation
• realistic COBOL programs
• structural fidelity