Automated Validation of COBOL to Java Transformation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909647263236096 |
|---|---|
| author | Kumar, Atul Saha, Diptikalyan Yasue, Toshikai Ono, Kohichi Krishnan, Saravanan Hans, Sandeep Satoh, Fumiko Mitchell, Gerald Kumar, Sachin |
| author_facet | Kumar, Atul Saha, Diptikalyan Yasue, Toshikai Ono, Kohichi Krishnan, Saravanan Hans, Sandeep Satoh, Fumiko Mitchell, Gerald Kumar, Sachin |
| contents | Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterpriselevel 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. We propose a framework and a tool to help validate the equivalence of COBOL and translated Java. The results can also help repair the code if there are some issues and provide feedback to the AI model to improve. We have developed a symbolic-execution-based test generation to automatically generate unit tests for the source COBOL programs which also mocks the external resource calls. We generate equivalent JUnit test cases with equivalent mocking as COBOL and run them to check semantic equivalence between original and translated programs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Validation of COBOL to Java Transformation Kumar, Atul Saha, Diptikalyan Yasue, Toshikai Ono, Kohichi Krishnan, Saravanan Hans, Sandeep Satoh, Fumiko Mitchell, Gerald Kumar, Sachin Software Engineering Artificial Intelligence Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterpriselevel 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. We propose a framework and a tool to help validate the equivalence of COBOL and translated Java. The results can also help repair the code if there are some issues and provide feedback to the AI model to improve. We have developed a symbolic-execution-based test generation to automatically generate unit tests for the source COBOL programs which also mocks the external resource calls. We generate equivalent JUnit test cases with equivalent mocking as COBOL and run them to check semantic equivalence between original and translated programs. |
| title | Automated Validation of COBOL to Java Transformation |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2506.10999 |