Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterprise-level code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-based automatic transformation are encouraging, the resulting code cannot be trusted to correctly translate the original code, making manual validation of translated Java code from COBOL a necessary but time-consuming and labor-intensive process. In this paper, we share our experience of developing a testing framework for IBM Watsonx Code Assistant for Z (WCA4Z) [5], an industrial tool designed for COBOL to Java translation. The framework automates the process of testing the functional equivalence of the translated Java code against the original COBOL programs in an industry context. Our framework uses symbolic execution to generate unit tests for COBOL, mocking external calls and transforming them into JUnit tests to validate semantic equivalence with translated Java. The results not only help identify and repair any detected discrepancies but also provide feedback to improve the AI model.

本研究解决了通过大型语言模型进行的COBOL到Java代码转换后，自动生成的代码准确性不可靠的问题。我们开发了一种测试框架，利用符号执行生成单元测试，以自动验证转换后Java代码与原COBOL程序的功能等效性。该框架的显著成果是能有效识别和修复代码不一致，并为改进AI模型提供反馈。

COBOL到Java转换的自动化测试