Authors :
Sahil Sanas; Arya Naik; Aditya Veerkar; Pooja T. Kohok
Volume/Issue :
Volume 10 - 2025, Issue 11 - November
Google Scholar :
https://tinyurl.com/32ayts2z
Scribd :
https://tinyurl.com/y4vamv44
DOI :
https://doi.org/10.38124/ijisrt/25nov074
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
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Abstract :
Legacy COBOL and C systems are still widely used in industries, yet they are increasingly costly, insecure, and
incompatible with modern platforms. Traditional modernization methods, although effective, often require significant time
and resources and carry high risks of disruption.
This paper proposes a scalable alternative that leverages Large Language Models (LLMs) within a structured multi-
agent framework, guided by the “7Rs of Modernization.” The frame- work comprises three agents: an Analysis Agent that
interprets and maps legacy code, a Coder Agent that generates modern equivalents, and a Review Agent that validates
correctness, security, and compliance through iterative feedback.
By automating much of the migration process, the proposed approach enables faster, more transparent, and less risky
mod- ernization. It helps enterprises transition from outdated systems to modular, secure, and cloud-ready solutions,
offering a cost- effective and future-proof pathway to digital transformation.
Keywords :
LLM, Legacy Code, Modernization, 7Rs of Mod- Ernization, Multi-Agent Framework, Software Migration, Reliabil- Ity, Performance.
References :
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- S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
- G. Bandarupalli, “Code Reborn: AI-Driven Legacy Systems Modern- ization from COBOL to Java,” arXiv preprint arXiv:2504.11335, Apr. 2025. Available: https://arxiv.org/abs/2504.11335
- J. Fitzpatrick, “Case Study: Converting C Programs to C++,” C++ Report, vol. 8, no. 2, p. 40, 1996.
- S. Froimovich, R. Gal, W. Ibraheem, and A. Ziv, “Quality Eval- uation of COBOL to Java Code Transformation,” arXiv preprint arXiv:2507.23356, Jul. 2025. Available: https://arxiv.org/abs/2507.23356
- L. Solovyeva, et al., “Leveraging LLMs for Automated Translation of Legacy Code: A Case Study on PL/SQL to Java Transformation” arXiv:2508.19663v1 [cs.SE] 27 Aug 2025. Available: https://arxiv.org/ html/2508.19663
- K. Cheng, X. Shen, Y. Yang, T. Wang, Y. Cao, M. A. Ali, H. Wang, L. Hu, D. Wang, “CODEMENV: Benchmarking Large Language Models on Code Migration,” arXiv preprint arXiv:2506.00894, 2025. Available: https://arxiv.org/abs/2506.00894
- A. T. V. Dau, H. T. Dao, A. T. Nguyen, et al., “XMainframe: A Large Language Model for Mainframe Modernization,” arXiv preprint arXiv:2408.04660, 2024. Available: https://arxiv.org/abs/2408.04660
- C. Ziftci, S. Nikolov, A. Sjo¨vall, B. Kim, D. Codecasa, M. Kim, et al., “Migrating Code At Scale With LLMs At Google,” arXiv preprint arXiv:2504.09691, 2025. Available: https://arxiv.org/pdf/2504.09691
- J. Ala-Salmi, et al., “Autonomous Multi-Agent Modernization of Legacy Web Applications,” arXiv preprint arXiv:2501.19204, 2025. Available: https://arxiv.org/abs/2501.19204
- C. Diggs, M. Doyle, A. Madan, S. Scott, E. Escamilla et al., “Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation” arXiv preprint arXiv:2411.14971, 2024. Available: https://arxiv.org/abs/2411.14971
Legacy COBOL and C systems are still widely used in industries, yet they are increasingly costly, insecure, and
incompatible with modern platforms. Traditional modernization methods, although effective, often require significant time
and resources and carry high risks of disruption.
This paper proposes a scalable alternative that leverages Large Language Models (LLMs) within a structured multi-
agent framework, guided by the “7Rs of Modernization.” The frame- work comprises three agents: an Analysis Agent that
interprets and maps legacy code, a Coder Agent that generates modern equivalents, and a Review Agent that validates
correctness, security, and compliance through iterative feedback.
By automating much of the migration process, the proposed approach enables faster, more transparent, and less risky
mod- ernization. It helps enterprises transition from outdated systems to modular, secure, and cloud-ready solutions,
offering a cost- effective and future-proof pathway to digital transformation.
Keywords :
LLM, Legacy Code, Modernization, 7Rs of Mod- Ernization, Multi-Agent Framework, Software Migration, Reliabil- Ity, Performance.