  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.
