# Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java

arXiv: https://arxiv.org/abs/2504.11335 · HTML: https://mlsanity.com/paper/2504.11335/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [MLOps: A Review](https://mlsanity.com/paper/2308.10908/) (2023)
- [AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainability](https://mlsanity.com/paper/2504.10412/) (2025)

## Built on by (most cited first, 4 of 4)

- [Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions](https://mlsanity.com/paper/2505.17094/) (2025)
- [The Impact of Software Testing with Quantum Optimization Meets Machine Learning](https://mlsanity.com/paper/2506.02090/) (2025)
- [Reinforcement Learning for Autonomous Warehouse Orchestration in SAP Logistics Execution: Redefining Supply Chain Agility](https://mlsanity.com/paper/2506.06523/) (2025)
- [Toward Self-Evolution-Ready Workflow Harnesses: A Reversible Migration Path and Convertibility Taxonomy for Expert LLM Pipelines](https://mlsanity.com/paper/2606.24598/) (2026)

## Abstract (arXiv)

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.