Ent is a system designed to leverage tree-edit operations for analyzing, modifying, and improving code architecture. The project aims to create an AI-driven issue/feature classifier for GitHub repositories, using advanced tree-edit distance calculations, memoization, and AI-generated code suggestions.
Our ultimate goal is to analyze the history of any Git repository, detect churn (repeated changes to specific areas), infer potential architecture improvements, and predict future issues or features.
This project aligns with the ideas discussed in the FASE 2010 paper on OperV, which explores operation-based version control. Ent extends those ideas by incorporating AI, allowing for intelligent decision-making around code changes and architecture predictions.
- Fine-grained and coarse-grained tree-edit operations (insert, delete, update).
- AI-assisted subtree hashing and edit sequence generation.
- Detection of churn points in large repositories.
- Prediction of future issues/features based on historical patterns.
- Implement core tree-edit operations using s-expressions.
- Integrate AI to generate edit sequences for complex tree manipulations.
- Dockerize the system for easy deployment.
Shoutout to the OperV paper, which inspired the core ideas of operation-based version control for efficient and scalable tree edits.