# Recommending Insightful Comments for Source Code using Crowdsourced Knowledge

arXiv: https://arxiv.org/abs/1807.02278 · HTML: https://mlsanity.com/paper/1807.02278/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [BERT-Based Sentiment Analysis: A Software Engineering Perspective](https://mlsanity.com/paper/2106.02581/) (2021)
- [Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions](https://mlsanity.com/paper/2502.14202/) (2025)
- [Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers](https://mlsanity.com/paper/2410.22663/) (2024)

## Abstract (arXiv)

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.