Technology Category
Natural Language Processing: Code Generation / Program Synthesis from Natural LanguageCognitive Modeling & Cognitive Systems: Simulating Human BehaviorConstraint Satisfaction and Optimization: Satisfiability Modulo Theories
Automated Testing of COBOL to Java Transformation
🤖 AI Summary
To address the challenges of verifying functional equivalence and the high cost and low reliability of manual validation in COBOL-to-Java automated translation, this paper proposes the first cross-language semantic equivalence verification framework integrating symbolic execution with external dependency simulation. The framework models COBOL programs and employs AST-driven test generation to automatically convert COBOL unit tests into JUnit tests, while leveraging mocking mechanisms to handle external calls. It further introduces an LLM-based feedback refinement loop to iteratively improve the underlying translation model. Experimental results demonstrate that the method automatically detects and precisely localizes semantic discrepancies, significantly enhancing the trustworthiness of AI-generated Java code. This framework has been successfully deployed in IBM Watsonx Code Assistant for Z (WCA4Z) to support enterprise-scale modernization of legacy COBOL systems on IBM Z platforms.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Ensuring functional equivalence between COBOL and translated Java code
Automating validation of AI-translated legacy enterprise code
Reducing manual effort in testing COBOL-to-Java transformation accuracy
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-based COBOL to Java translation automation
Symbolic execution for COBOL unit test generation
JUnit tests validate semantic equivalence
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