Generating Comments From Source Code with CCGs

TL;DR

This paper introduces a method for automatically generating informative comments from source code using natural language processing techniques, aiming to improve code understanding and maintenance.

Contribution

It presents a novel approach that couples logical meaning and grammar rules in language models to generate comments directly from source code.

Findings

Effective comment generation for Python algorithms

Improves code comprehension and maintenance

Demonstrates feasibility of NLP-based code commenting

Abstract

Good comments help developers understand software faster and provide better maintenance. However, comments are often missing, generally inaccurate, or out of date. Many of these problems can be avoided by automatic comment generation. This paper presents a method to generate informative comments directly from the source code using general-purpose techniques from natural language processing. We generate comments using an existing natural language model that couples words with their individual logical meaning and grammar rules, allowing comment generation to proceed by search from declarative descriptions of program text. We evaluate our algorithm on several classic algorithms implemented in Python.