Leveraging Finetuned LLMs to Modernize COBOL Systems

For decades, COBOL applications have formed the core of mission-critical systems across banking, insurance, government, and manufacturing. As enterprises lean into cloud computing, agile development, and continuous delivery, these legacy systems increasingly show their age. Translating COBOL into more flexible, contemporary languages such as Java often involves tedious, time-consuming work. It requires dissecting decades-old code, unraveling intricate business rules, and painstakingly re-implementing them in modern frameworks. Even small errors in this manual process can have outsized consequences, and the looming retirement of COBOL veterans compounds the risk. Younger engineers, while adept in modern languages, may lack exposure to the architectural patterns, data handling, and batch processes characteristic of legacy mainframe code.

Finetuning large language models (LLMs) for COBOL-to-Java transformation offers a path out of these challenges. By providing the model with carefully aligned examples—pairs of COBOL and equivalent Java snippets, complete with business logic annotations—it learns to translate code not just syntactically, but semantically. Over time, this training helps the model identify how high-level concepts in COBOL map to modern frameworks, enabling it to produce meaningful, structurally sound Java drafts. In particular, we propose a systematic methodology for curating these training examples, ensuring that even the most intricate business logic is accurately preserved. Additionally, we suggest leveraging domain experts and senior COBOL developers to guide this curation, capturing nuanced architectural patterns and ensuring that core logic transitions seamlessly into the new environment.

From a technical standpoint, finetuning can leverage advanced parameter-efficient techniques such as LoRA (Low-Rank Adaptation) or adapter layers. In contrast to retraining the entire model—an expensive endeavor requiring massive computational resources—these methods inject a small number of trainable parameters that subtly adjust the model’s internal representations. By fine-tuning only these lightweight components, teams achieve faster training times, lower hardware requirements, and more iterative experimentation. We propose incorporating continuous integration (CI) pipelines that automatically evaluate intermediate model outputs against known-good translations, ensuring rapid feedback and incremental improvements without massive infrastructure overhead.

Once trained, the model assists engineers by generating preliminary Java code, dramatically reducing manual rewriting. Although developers still must review outputs, run tests, and confirm correctness, the assistance from the model often shortens development cycles. It also helps bridge the knowledge gap for newer team members, illustrating how legacy constructs map to contemporary design patterns and frameworks. Over time, with additional feedback loops and continuous refinements, the LLM’s performance improves even further, leading to smoother, more predictable migrations. To maintain sustained progress, we suggest instituting periodic review sessions where project leads and architects evaluate the model’s latest outputs and feed lessons learned back into the training process.

Beyond the initial modernization phase, finetuned models remain valuable. They can assist with ongoing maintenance, optimizations, and regulatory compliance updates, understanding not only how the original system worked but also how it should evolve. In this way, LLMs serve as long-term allies in the dynamic landscape of enterprise IT, reducing dependence on scarce COBOL expertise while boosting confidence in the integrity and maintainability of the re-engineered systems.

Through embracing finetuned LLMs and parameter-efficient training strategies, we can reimagine the modernization journey. Rather than viewing it as a costly, one-off gamble, they can treat it as an iterative, model-assisted evolution. As these techniques mature, enterprises will find transitioning from monolithic COBOL systems to agile, cloud-native architectures more achievable. Consequently, they ensure that their core business logic can thrive in a world where adaptability, responsiveness, and innovation matter significantly more than ever.