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.
Wed 17 MayDisplayed time zone: Hobart change
15:45 - 17:15 | DocumentationTechnical Track / Journal-First Papers at Level G - Plenary Room 1 Chair(s): Denys Poshyvanyk College of William and Mary | ||
15:45 15mTalk | Developer-Intent Driven Code Comment Generation Technical Track Fangwen Mu Institute of Software Chinese Academy of Sciences, Xiao Chen Institute of Software Chinese Academy of Sciences, Lin Shi ISCAS, Song Wang York University, Qing Wang Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences Pre-print | ||
16:00 15mTalk | Data Quality Matters: A Case Study of ObsoleteComment Detection Technical Track Shengbin Xu Nanjing University, Yuan Yao Nanjing University, Feng Xu Nanjing University, Tianxiao Gu TikTok Inc., Jingwei Xu , Xiaoxing Ma Nanjing University Pre-print | ||
16:15 15mTalk | Revisiting Learning-based Commit Message Generation Technical Track Jinhao Dong Peking University, Yiling Lou Fudan University, Dan Hao Peking University, Lin Tan Purdue University Pre-print | ||
16:30 15mTalk | Commit Message Matters: Investigating Impact and Evolution of Commit Message Quality Technical Track | ||
16:45 7mTalk | On the Significance of Category Prediction for Code-Comment Synchronization Journal-First Papers Zhen Yang City University of Hong Kong, China, Jacky Keung City University of Hong Kong, Xiao Yu Wuhan University of Technology, Yan Xiao National University of Singapore, Zhi Jin Peking University, Jingyu Zhang City University of Hong Kong | ||
16:52 7mTalk | Correlating Automated and Human Evaluation of Code Documentation Generation Quality Journal-First Papers Xing Hu Zhejiang University, Qiuyuan Chen Zhejiang University, Haoye Wang Hangzhou City University, Xin Xia Huawei, David Lo Singapore Management University, Thomas Zimmermann Microsoft Research | ||
17:00 7mTalk | Predictive Comment Updating with Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach Journal-First Papers Bo Lin National University of Defense Technology, Shangwen Wang National University of Defense Technology, Zhongxin Liu Zhejiang University, Xin Xia Huawei, Xiaoguang Mao National University of Defense Technology Link to publication DOI Pre-print |