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.

本研究提出了一种使用开源项目的修订历史自动提取有监督数据并为此任务提供手动注释评估数据集的方法，通过构建丰富的特征集，包括代码、注释及其之间的关系，开发了二元分类器和序列标记模型，从而实现Java代码中Javadoc注释实体与代码元素的关联性分析，并获得比多个基线方法更好的性能。

将自然语言注释与源代码实体关联