CodeBuff
A universal code formatter that uses machine learning to learn formatting patterns from a corpus of existing code.
What is CodeBuff?
CodeBuff is a research project and tool that uses machine learning to automatically format source code. It learns formatting patterns—such as indentation, spacing, and line breaks—from a corpus of existing, well-formatted code, then applies those patterns to new, unformatted code. It aims to solve the problem of building and maintaining language-specific formatters by providing a universal, corpus-driven approach.
Researchers in programming languages and software engineering, as well as developers interested in automated code formatting tools, particularly those working with multiple languages or large codebases where consistent formatting is challenging.
CodeBuff offers a novel, data-driven alternative to traditional rule-based formatters, potentially reducing the manual effort needed to create and tune formatters for different languages. Its ability to learn from any code corpus allows it to adapt to project-specific or team-specific coding styles.
Overview
Language-agnostic pretty-printing through machine learning (uh, like, is this possible? YES, apparently).
Use Cases
Best For
- Researching machine learning applications in code formatting and style analysis
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