Recommending Insightful Comments for Source Code using Crowdsourced Knowledge

TL;DR

This paper presents a novel approach to recommending insightful comments about code quality and issues by mining Stack Overflow discussions, enhancing program comprehension beyond traditional functionality descriptions.

Contribution

It introduces a heuristic-based method to extract valuable insights from crowdsourced discussions for code comment recommendation, addressing a gap in existing techniques.

Findings

Achieved 85.42% recall in comment retrieval

User study confirms accuracy and usefulness of recommendations

Demonstrated effectiveness on 292 code segments

Abstract

Recently, automatic code comment generation is proposed to facilitate program comprehension. Existing code comment generation techniques focus on describing the functionality of the source code. However, there are other aspects such as insights about quality or issues of the code, which are overlooked by earlier approaches. In this paper, we describe a mining approach that recommends insightful comments about the quality, deficiencies or scopes for further improvement of the source code. First, we conduct an exploratory study that motivates crowdsourced knowledge from Stack Overflow discussions as a potential resource for source code comment recommendation. Second, based on the findings from the exploratory study, we propose a heuristic-based technique for mining insightful comments from Stack Overflow Q & A site for source code comment recommendation. Experiments with 292 Stack Overflow code segments and 5,039 discussion comments show that our approach has a promising recall of 85.42%. We also conducted a complementary user study which confirms the accuracy and usefulness of the recommended comments.