Modernizing Legacy Code with AI-Driven Templates
The major trend highlighted across the blog post is the structured, template-driven use of AI—specifically OpenAI’s Codex—to modernize legacy codebases such as those built in COBOL. The approach is defined by well-documented, repeatable phases: planning, targeted pilot selection, comprehensive discovery, robust design and validation, and iterative implementation with continuous comparison to legacy systems. Reusability, clear documentation, and output validation are emphasized to ensure consistent, scalable modernization across projects.
New Cookbook Recipes
code_modernization.md
Source: openai/openai-cookbook
The blog post discusses the use of OpenAI’s Codex to modernize legacy codebases, focusing specifically on a COBOL-based investment portfolio system. It outlines a structured approach to modernization through five phases:
- Setup: Establishing a lightweight planning framework with AGENTS and PLANS documentation.
- Pilot Selection: Choosing a bounded pilot flow to create an ExecPlan detailing the modernization process.
- Inventory and Discovery: Capturing the functionalities of the chosen flow without delving into the original code.
- Design and Validation: Defining the target architecture, developing an API design, and establishing a testing strategy to ensure parity between legacy and modern implementations.
- Implementation and Comparison: Developing the modern code, running it alongside the legacy system, and validating output consistency.
The post concludes by emphasizing the creation of reusable templates for further modernization efforts, ensuring clarity and consistency in the process.