Generative AI (GenAI) and large language models (LLMs) can help developers write and understand code. Help with understanding code is especially useful in the case of legacy codebases with poor, out-of-date or misleading documentation. Since we last wrote about this, techniques and products for using GenAI to understand legacy codebases have further evolved, and we've successfully used some of them in practice, notably to assist reverse engineering efforts for mainframe modernization. A particularly promising technique we've used is a retrieval-augmented generation (RAG) approach where the information retrieval is done on a knowledge graph of the codebase. The knowledge graph can preserve structural information about the codebase beyond what an LLM could derive from the textual code alone. This is particularly helpful in legacy codebases that are less self-descriptive and cohesive. An additional opportunity to improve code understanding is that the graph can be further enriched with existing and AI-generated documentation, external dependencies, business domain knowledge or whatever else is available that can make the AI's job easier.
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 This is particularly useful in the case of legacy codebases that aren’t well-documented or where the documentation is outdated or misleading.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.
