Moving Mountains: Migrating Legacy Code in Weeks instead of Years

David Stein discusses an innovative approach to migrating legacy code using AI technologies, aiming to reduce the timeline from years to weeks.

Key Points:

• Context and Challenges: Stein explains the common challenges faced during large-scale legacy code migrations, such as technical debt and hidden dependencies.
• Approach: The process involves decomposing the problem into manageable, verifiable steps, using state-of-the-art coding Large Language Models (LLMs) to understand the codebase.
• Framework:
  • Decompose: Break down the task into smaller, executable steps.
  • Standardize: Establish a rigid validation process to ensure accuracy at each step.
  • Automate: Use coding LLMs to automate processes that were traditionally manual, creating an 'assembly line' for code migration.
• Execution: By equipping AI agents with tools and validation strategies like Snow SQL, Stein illustrates how agents can efficiently complete tasks with high certainty.
• Validation Strategy: A rigorous validation framework is essential to ensure each task is completed accurately, preventing the project from derailing.

Conclusion:

This talk underscores the potential of AI-driven methodologies to revolutionize the management of large-scale software migrations, transforming a traditionally length process into a more streamlined and efficient effort.

Thoughts and Implications:

• This approach highlights the significance of advanced AI tools in solving complex engineering challenges, emphasizing the need for robust validation mechanisms to leverage these technologies effectively.
• The methodology introduces a paradigm shift in how engineering teams can approach massive codebase transitions, offering insights into the future of software development in the AI era.

This is the end of the AI-generated content.

Recently we've been working on migrating our enterprise reporting application to a modern open-source metrics store. Historically, large-scale legacy migrations have been some of the most challenging projects in software. The weight of hundreds of thousands of lines of production code full of complexity and technical debt makes it extremely difficult to even evaluate new architectures, let alone deliver a fully rearchitected solution.

But this changed in early-to-mid 2025. We found that state-of-the-art coding LLMs are now powerful enough to deeply understand our legacy codebase. Although AI coding agents are not yet a push-button solution for problems of this magnitude, we found simple strategies for orchestrating AI agents effectively, enabling us to "move mountains" of technical debt and accomplish in days and weeks what used to take months and years.