# Developer-Intent Driven Code Comment Generation

arXiv: https://arxiv.org/abs/2302.07055 · HTML: https://mlsanity.com/paper/2302.07055/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2023.

## Built on by (most cited first, 16 of 16)

- [MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution](https://mlsanity.com/paper/2403.17927/) (2024)
- [Source Code Summarization in the Era of Large Language Models](https://mlsanity.com/paper/2407.07959/) (2024)
- [Can LLMs Generate Architectural Design Decisions? -An Exploratory Empirical study](https://mlsanity.com/paper/2403.01709/) (2024)
- [Enhancing LLM Performance Through Debate: An Empirical Study on Multi-Agent Debate for Coding Tasks](https://mlsanity.com/paper/2503.12029/) (2025)
- [Natural Language Outlines for Code: Literate Programming in the LLM Era](https://mlsanity.com/paper/2408.04820/) (2024)
- [Small Models, Big Tasks: An Exploratory Empirical Study on Small Language Models for Function Calling](https://mlsanity.com/paper/2504.19277/) (2025)
- [Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs](https://mlsanity.com/paper/2501.07857/) (2025)
- [Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks](https://mlsanity.com/paper/2504.19444/) (2025)
- [Issue-Oriented Agent-Based Framework for Automated Review Comment Generation](https://mlsanity.com/paper/2511.00517/) (2025)
- [MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generation](https://mlsanity.com/paper/2403.17357/) (2024)
- [Icing on the Cake: Automatic Code Summarization at Ericsson](https://mlsanity.com/paper/2408.09735/) (2024)
- [Selective Shot Learning for Code Explanation](https://mlsanity.com/paper/2412.12852/) (2024)
- [AI-Mediated Code Comment Improvement](https://mlsanity.com/paper/2505.09021/) (2025)
- [Issue Retrieval and Verification Enhanced Supplementary Code Comment Generation](https://mlsanity.com/paper/2506.14649/) (2025)
- [On the Impact of Code Comments for Automated Bug-Fixing: An Empirical Study](https://mlsanity.com/paper/2601.23059/) (2026)
- [Precision in Practice: Knowledge Guided Code Summarizing Grounded in Industrial Expectations](https://mlsanity.com/paper/2602.03400/) (2026)

## Abstract (arXiv)

Existing automatic code comment generators mainly focus on producing a general description of functionality for a given code snippet without considering developer intentions. However, in real-world practice, comments are complicated, which often contain information reflecting various intentions of developers, e.g., functionality summarization, design rationale, implementation details, code properties, etc. To bridge the gap between automatic code comment generation and real-world comment practice, we define Developer-Intent Driven Code Comment Generation, which can generate intent-aware comments for the same source code with different intents. To tackle this challenging task, we propose DOME, an approach that utilizes Intent-guided Selective Attention to explicitly select intent-relevant information from the source code, and produces various comments reflecting different intents. Our approach is evaluated on two real-world Java datasets, and the experimental results show that our approach outperforms the state-of-the-art baselines. A human evaluation also confirms the significant potential of applying DOME in practical usage, enabling developers to comment code effectively according to their own needs.