AI for Mass-Scale Code Refactoring and Analysis

Chapter 2. Techniques for Scaling AI for Large Codebases

As we look at our AI world of infinite interns applied to mass-scale code refactoring and analysis, we must look at how to supervise our interns more effectively. How can we build guardrails for them to work faster with a higher accuracy rate? In addition, how can we ensure the most efficient, cost-effective AI implementations for working on massive codebases?

One thing to keep in mind is that AI models have knowledge gaps when it comes to large codebases. A model cannot code using a framework it has never been trained on. Additionally, they are not trained on private data, such as a company’s codebase, out of the box. Processing codebases with millions of lines of code would overwhelm the model, leading to increased latency and noise that affects accuracy, assuming the model is even capable of managing such a large volume. New and helpful data beyond the prompt must be provided as context to the model in real time for fully understanding and refactoring large codebases.

Tip

A long context window for an LLM creates a challenge for the model to find information. The model must sift through a large amount of data to locate relevant information, which can be akin to finding a needle in a haystack. In general, as the context length increases, the model’s ability to maintain coherence and relevance diminishes.¹ This is due to the model managing more distractions and irrelevant information, which can cloud the focus on the primary ...