LLM-Powered Legacy Code Modernization


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

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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 It helps enterprises transition from outdated systems to modular, secure, and cloud-ready solutions, offering a cost- effective and future-proof pathway to digital transformation.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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  11. 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.

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Paper Submission Last Date
30 - November - 2025

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