How LLMs Could Help Migrate Legacy Systems

Modernizing legacy codebases is no longer optional—it’s essential for organizations seeking agility, security, and long-term scalability. However, this transformation is often fraught with challenges. This article explores the technical, financial, and operational hurdles of legacy code migration and highlights how Large Language Models (LLMs) are changing the game. From understanding cryptic code to automating translation and testing, you’ll learn how AI is becoming a powerful ally in navigating legacy modernization projects.

LLM in Legacy Code Migration: Key Insights

• Legacy migrations are complex, risky, and costly, often involving outdated technologies and undocumented code.
• LLMs such as GPT-4 can dramatically streamline the migration process by analyzing code, generating documentation, and translating across languages and frameworks.
• LLMs improve migration quality and speed, but human oversight remains essential, especially for edge cases and domain-specific logic.
• Strategic planning and compliance must guide AI-assisted efforts to ensure secure, maintainable, and regulation-ready outcomes.
• The future of code migration is AI-augmented, with LLMs playing a central role in end-to-end modernization pipelines.

The Challenges and Costs of Legacy Code Migration

Migrating legacy codebases is a complex and costly endeavor – one that continues to burden organizations striving to modernize their IT infrastructure. Legacy systems often rely on outdated technologies, monolithic architectures, and aging development practices, making them difficult to understand, maintain, and integrate with modern platforms.

The common pain points include:

• Complexity
  Legacy code is frequently poorly documented, tightly coupled, and built on obsolete frameworks, all of which significantly complicate reverse engineering and migration planning.
• Risk
  Any modification to legacy systems can introduce unintended bugs or outages, posing a serious threat to business continuity and service availability.
• Time and Cost
  Manual migration requires considerable effort, domain expertise, and cross-functional coordination – often stretching project timelines and inflating budgets. According to Oracle, over 80% of data migration projects fail to meet their deadlines or remain within budget, largely due to these inherent complexities.¹
• Compatibility Issues
  Legacy applications may use proprietary standards, deprecated libraries, or unsupported protocols, making them fundamentally incompatible with modern infrastructure and requiring extensive code rewrites or architectural refactoring.⁶
• Data Integrity and Downtime Risks
  Migrating large volumes of production data without loss, duplication, or corruption remains a high-stakes challenge. Additionally, prolonged downtime during the transition can severely impact operational performance and customer experience.²

Despite these challenges, the business drivers for modernization are compelling: improving agility, strengthening security, ensuring regulatory compliance, reducing technical debt, and enabling seamless integration with cloud-native, API-driven, and distributed architectures.

Introducing LLMs as a Powerful Migration Tool

Large Language Models (LLMs) such as GPT-4, Code Llama, and other domain-specialized variants are emerging as powerful tools to address the long-standing challenges of legacy code migration. These models bring a new layer of intelligence to modernization efforts by:

Understanding and analyzing code: LLMs can parse complex, poorly documented legacy codebases, infer business logic, and generate missing or outdated documentation—bridging knowledge gaps that often stall migration initiatives.

Translating code across languages and frameworks: These models are capable of converting legacy code written in older languages like COBOL, VB6, or C++ into modern programming languages such as Java, C#, or Python. Importantly, they can adapt the output to reflect current development standards and architectural best practices.⁵

Comprehending code context: Unlike basic syntax converters, LLMs understand surrounding context—including variable usage, control flow, and external dependencies—enabling more accurate, maintainable, and meaningful code translation that preserves business functionality.

By automating and augmenting traditionally manual, error-prone tasks, LLMs are redefining what’s possible in modernization timelines, accuracy, and scalability.

Potential Use Cases for LLMs in Code Migration

Large Language Models (LLMs) are proving to be versatile assets in legacy modernization initiatives, offering support across a wide range of tasks that have traditionally required intensive manual effort. Their utility spans from initial code comprehension to post-migration validation, making them a valuable co-pilot throughout the transformation journey.

One of the most impactful applications is in code understanding and documentation generation. Many legacy systems suffer from a lack of up-to-date documentation, making onboarding and refactoring extremely difficult. LLMs can analyze these codebases and generate accurate, human-readable summaries, inline comments, and architectural overviews, bridging the gap between tribal knowledge and maintainable design. This accelerates team understanding and de-risks the migration planning phase.

LLMs also enable automated code translation and intelligent refactoring. Unlike traditional transpilers that perform one-to-one syntax mapping, LLMs understand business intent and design patterns embedded in the code. They can not only rewrite applications from legacy languages like COBOL, Perl, or VB6 into modern equivalents such as Python, Java, or TypeScript, but also restructure the output to follow modular, maintainable architectures aligned with modern software engineering principles.

Another critical use case lies in identifying compatibility and architectural issues. LLMs can flag deprecated APIs, unsupported libraries, and architectural constructs that are incompatible with target environments—such as containerized infrastructure, microservices, or serverless platforms. This early-stage insight reduces surprises during deployment and minimizes the need for last-minute architectural workarounds.

Finally, LLMs can significantly enhance test coverage during migration. By generating targeted unit tests and integration test scaffolds for the refactored code, LLMs help ensure functional equivalence with the legacy system and reduce the risk of regressions. These tests serve as a quality gate and confidence booster, especially in large-scale migrations where manual testing alone is insufficient or cost-prohibitive.

Collectively, these use cases illustrate that LLMs are not simply code converters—they are intelligent enablers that augment developer workflows, reduce risk, and accelerate time-to-value in modernization projects.

Benefits and Considerations of LLM-Assisted Migration

Integrating Large Language Models into legacy code migration initiatives offers a range of compelling benefits. First and foremost is speed: by automating key aspects of translation, refactoring, and documentation generation, LLMs can significantly compress project timelines that would otherwise stretch into months or years. What traditionally required weeks of manual effort—such as reviewing obsolete code, untangling undocumented logic, or rewriting to meet modern syntax—can now be accelerated through AI-assisted workflows.

In parallel, this reduction in manual effort allows engineering teams to reallocate their time and expertise toward more strategic activities. Instead of performing tedious, repetitive tasks, developers and architects can focus on system design, validation, and integration – areas where human judgment is essential and where LLMs act as powerful enablers rather than replacements.

Moreover, LLMs contribute directly to code quality during the migration process. By enforcing modern coding standards, generating inline documentation, and scaffolding test cases, these models help ensure that the modernized codebase is not only functional but maintainable and aligned with contemporary best practices. This mitigates the risk of recreating legacy problems in a new language or framework.

That said, successful adoption requires acknowledging and planning for key considerations. One primary concern is accuracy. While LLMs are remarkably capable, they may struggle with edge cases, deeply entangled business logic, or domain-specific patterns that were never documented or standardized. As a result, human oversight remains essential – especially during validation and integration phases.

Equally important is the need for strategic project planning. LLMs are not a silver bullet; their use must be embedded within a broader modernization strategy that includes robust version control, test coverage, stakeholder alignment, and risk mitigation practices. Without a clear roadmap and cross-functional coordination, even AI-assisted efforts can falter.

Finally, security and compliance must be front and center. Migrating legacy code presents a unique opportunity to modernize not just language but posture. With increasing emphasis on memory-safe languages like Rust, especially in regulated industries, organizations must critically assess whether LLM-generated code meets evolving security and compliance standards. Furthermore, when using AI models – especially in hosted environments – data privacy, IP protection, and model provenance must be taken into account.

In short, while LLMs can supercharge modernization efforts, realizing their full value requires thoughtful integration into engineering workflows, project governance, and compliance frameworks.

The Future of Legacy Modernization with AI

As AI capabilities continue to mature, the role of Large Language Models in legacy modernization is poised to expand significantly. Future LLM-powered tools will likely become deeply embedded within integrated development environments (IDEs), offering engineers real-time migration suggestions, automated refactoring, and intelligent prompts that adapt to the structure and context of the codebase. These systems will also evolve through continuous learning, leveraging insights from past migrations to improve accuracy, reduce hallucinations, and better accommodate industry-specific architectural patterns and compliance needs.

Perhaps most transformative, LLMs will help shape end-to-end modernization pipelines—orchestrating code analysis, translation, documentation, test generation, and even deployment workflows, all under human oversight. This holistic approach will reduce migration complexity and democratize modernization, making it viable for a broader range of teams and organizations.

Ultimately, this evolution will make legacy modernization not only faster and more cost-effective, but also more strategic—unlocking the full value of long-standing software investments while accelerating the shift toward agile, secure, and cloud-native architectures.

LLM in Legacy Code Migration – FAQ

Q: How can Large Language Models (LLMs) help with migration?

A: LLMs like GPT-4 can analyze, translate, and refactor code, generate documentation, identify compatibility issues, and create tests—dramatically reducing manual effort and improving migration quality.

Q: Can LLMs fully automate legacy code migration?

A: No. While LLMs can accelerate and enhance many tasks, human oversight is still necessary for validation, integration, and ensuring that business logic and compliance requirements are correctly preserved.

Q: Are there any risks in using AI for code migration?

A: Yes. Potential risks include inaccuracies in complex or domain-specific code, data privacy concerns, and the need to ensure LLM-generated code meets security and compliance standards.

Q: What types of legacy languages can LLMs help modernize?

A: LLMs can assist with modernizing a variety of legacy languages such as COBOL, VB6, Perl, and older versions of C/C++, translating them into modern languages like Java, Python, C#, or TypeScript.

Q: Do organizations need special infrastructure to use LLMs for migration?

A: Not necessarily. Many LLMs are accessible through cloud APIs or integrated into developer tools. However, organizations should ensure proper governance, version control, and security policies are in place for effective adoption.

References

• Oracle, “Top 10 Data Migration Challenges in 2025” — used in “Challenges and Costs of Legacy Code Migration” to highlight project failure rates and complexity.
  https://forbytes.com/blog/common-data-migration-challenges/
• Kellton, “Data Migration Trends in 2025 & Challenges to Solve” – referenced in “Challenges and Costs of Legacy Code Migration” for data integrity and downtime risks.
  https://www.kellton.com/kellton-tech-blog/revealing-top-data-migration-trends
• Aalpha, “Legacy Application Migration Guide – 2025” — used in “Challenges and Costs” and “Introducing LLMs” for complexity, compatibility issues, and code refactoring strategies.
  https://www.aalpha.net/blog/legacy-application-migration/
• LinkedIn, “Address Legacy Code Now: Why Companies Must Act” — cited in “Introducing LLMs” and “Benefits and Considerations” regarding security risks and the need for migration to memory-safe languages.
  https://www.linkedin.com/pulse/time-address-legacy-code-now-why-companies-must-act-before-tisi-kd8je
• Tencent Cloud, “What are some of the biggest challenges with legacy migration?” — referenced in “Potential Use Cases” and “Benefits and Considerations” for compatibility, data migration, and security concerns.
  https://www.tencentcloud.com/techpedia/100252
• EntheosWeb, “Common Challenges in Legacy App Migration” — used in “Benefits and Considerations” to emphasize the need for human oversight and strategic planning.
  https://www.entheosweb.com/common-challenges-in-legacy-app-migration/