Generative AI (GenAI) and large language models (LLMs) can help developers both write and understand code. In practical application, this is so far mostly limited to smaller code snippets, but more products and technology developments are emerging for using GenAI to understand legacy codebases. This is particularly useful in the case of legacy codebases that aren’t well-documented or where the documentation is outdated or misleading. For example, Driver AI or bloop use RAG approaches that combine language intelligence and code search with LLMs to help users find their way around a codebase. Emerging models with larger and larger context windows will also help to make these techniques more viable for sizable codebases. Another promising application of GenAI for legacy code is in the space of mainframe modernization, where bottlenecks often form around reverse engineers who need to understand the existing codebase and turn that understanding into requirements for the modernization project. Using GenAI to assist those reverse engineers can help them get their work done faster.
