Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916691788693504 |
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| author | Bandarupalli, Gopichand |
| author_facet | Bandarupalli, Gopichand |
| contents | This study investigates AI-driven modernization of legacy COBOL code into Java, addressing a critical challenge in aging software systems. Leveraging the Legacy COBOL 2024 Corpus -- 50,000 COBOL files from public and enterprise sources -- Java parses the code, AI suggests upgrades, and React visualizes gains. Achieving 93% accuracy, complexity drops 35% (from 18 to 11.7) and coupling 33% (from 8 to 5.4), surpassing manual efforts (75%) and rule-based tools (82%). The approach offers a scalable path to rejuvenate COBOL systems, vital for industries like banking and insurance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11335 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java Bandarupalli, Gopichand Software Engineering Artificial Intelligence Machine Learning This study investigates AI-driven modernization of legacy COBOL code into Java, addressing a critical challenge in aging software systems. Leveraging the Legacy COBOL 2024 Corpus -- 50,000 COBOL files from public and enterprise sources -- Java parses the code, AI suggests upgrades, and React visualizes gains. Achieving 93% accuracy, complexity drops 35% (from 18 to 11.7) and coupling 33% (from 8 to 5.4), surpassing manual efforts (75%) and rule-based tools (82%). The approach offers a scalable path to rejuvenate COBOL systems, vital for industries like banking and insurance. |
| title | Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.11335 |