跳到正文
arXiv cs.AI· Andras L. Ferenczi, Mariya Bessonov, Allwyn Ponnudurai, Chandrakanth R. Mamillapalli, Krishna K. Maddula, Krishna K. Lingamneni·· 12 小时前AI 评分31

为 COBOL 到 Java 迁移提供可重复的入口重放见证认证

Certifying Repeatable Entry-Replay Witnesses for COBOL-to-Java Migration

AI 导读

研究提出一种通过入口重放测试用例认证生成合成端到端测试的方法,在 33,429 行 COBOL 程序上生成 849 个测试用例,覆盖 78.1% 源码分支和 95.7% 主 AST 段落。

正文 · 原文

View PDF HTML (experimental)

Abstract:Producing synthetic data for comparison testing a migrated COBOL application is a major challenge that agentic migrations face today. Our prior paper, the Agentic Method for Deterministic Validation of Legacy Code Migration, presents a deterministic oracle to facilitate such migrations, the Locksmith Loop. We now propose a novel method for achieving high branch and paragraph coverage through generated synthetic end-to-end test cases, by means of entry-replay test-case certification by observing the code execution trace on a surrogate COBOL program that mocks external interactions via a chronological external-operation tape. We applied our method to a very large COBOL program with 33,429 lines of code to generate 849 synthetic end-to-end test cases covering 2,973/3,806 source branches (78.1%) and 989/1,033 main-AST paragraphs (95.7%). Using the Locksmith Loop's projected-agreement validation, the generated Java code matched the COBOL surrogate on the specified observations across all 849 certified synthetic end-to-end test cases. During the campaign we detected 61 defects and fixed the Java generator accordingly. To test whether the coding agent could silently introduce business logic bugs, we seeded 450 defects into the Java code, such as deleted writes, deleted paragraph calls, and altered rounding, to verify how many of these would be detected by the gating methodology. The gate detected 47.6%, including all deleted writes and paragraph calls in executed code, but was unable to detect the rounding and precision defects.
Comments: 8 pages, 9 figures, 4 tables
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
ACM classes: D.2.5; D.2.7
Cite as: arXiv:2610.27272 [cs.SE]
  (or arXiv:2610.27272v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2610.27272

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Andras Ferenczi [view email]
[v1] Mon, 19 Oct 2026 20:44:35 UTC (218 KB)

来源:arXiv cs.AI · arxiv.org