Associating Natural Language Comment and Source Code Entities

TL;DR

This paper presents methods to automatically associate Java source code elements with their corresponding Javadoc comment entities, improving code-comment understanding using supervised learning models trained on revision history data.

Contribution

It introduces a novel approach for extracting supervised data from revision histories and develops models for associating code with comments, with a new annotated dataset for evaluation.

Findings

Models outperform baselines in association accuracy

Supervised data extraction from revision history is effective

Rich feature sets improve classification performance

Abstract

Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be useful in improving code comprehensibility and maintaining the consistency between code and comments. As an initial step towards this larger goal, we address the task of associating entities in Javadoc comments with elements in Java source code. We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. We develop a binary classifier and a sequence labeling model by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them. Experiments show that our systems outperform several baselines learning from the proposed supervision.