mlsanityData to 14 Oct 2026

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

arXiv 2504.11335 ↗First posted 15 Apr 2025cs.SE cs.AI cs.LGcited by 4

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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.

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

2 of 25 references.

References · 2

  1. 2023MLOps: A Reviewcited by 16
  2. 2025AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainabilitycited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusionscited by 0
  2. 2025The Impact of Software Testing with Quantum Optimization Meets Machine Learningcited by 0
  3. 2025Reinforcement Learning for Autonomous Warehouse Orchestration in SAP Logistics Execution: Redefining Supply Chain Agilitycited by 0
  4. 2026Toward Self-Evolution-Ready Workflow Harnesses: A Reversible Migration Path and Convertibility Taxonomy for Expert LLM Pipelinescited by 0