Proceedings Article10.1109/MSR.2019.00056
Generating commit messages from diffs using pointer-generator network
Qin Liu,Zihe Liu,Hongming Zhu,Hongfei Fan,Bowen Du,Yu Qian +5 more
- 26 May 2019
- pp 299-309
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TL;DR: PtrGNCMsg, a novel approach which is based on an improved sequence-to-sequence model with the pointer-generator network to translate code diffs into commit messages outperforms recent approaches based on neural machine translation, and first enables the prediction of OOV words.
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Abstract: The commit messages in source code repositories are valuable but not easy to be generated manually in time for tracking issues, reporting bugs, and understanding codes. Recently published works indicated that the deep neural machine translation approaches have drawn considerable attentions on automatic generation of commit messages. However, they could not deal with out-of-vocabulary (OOV) words, which are essential context-specific identifiers such as class names and method names in code diffs. In this paper, we propose PtrGNCMsg, a novel approach which is based on an improved sequence-to-sequence model with the pointer-generator network to translate code diffs into commit messages. By searching the smallest identifier set with the highest probability, PtrGNCMsg outperforms recent approaches based on neural machine translation, and first enables the prediction of OOV words. The experimental results based on the corpus of diffs and manual commit messages from the top 2,000 Java projects in GitHub show that PtrGNCMsg outperforms the state-of-the-art approach with improved BLEU by 1.02, ROUGE-1 by 4.00 and ROUGE-L by 3.78, respectively.
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Citations
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
TL;DR: Zhang et al. as discussed by the authors propose a commit message generation model, named ATOM, which explicitly incorporates the abstract syntax tree for representing code changes and integrates both retrieved and generated messages through hybrid ranking.
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation
Jinhao Dong,Yiling Lou,Qihao Zhu,Zeyu Sun,Zhilin Li,Wenjie Zhang,Dan Hao +6 more
- 01 May 2022
TL;DR: A novel commit message generation technique, FIRA, which first represents code changes via fine-grained graphs and then learns to generate commit messages automati-cally, which outperforms state-of-the-art techniques in terms of BLEU, ROUGE-L, and METEOR.
61
Context-aware Retrieval-based Deep Commit Message Generation
TL;DR: CoRec as discussed by the authors uses a context-aware encoder-decoder model that randomly selects the previous output of the decoder or the embedding vector of a ground truth word as context to make the model gradually aware of previous alignment choices.
55
BugSum: Deep Context Understanding for Bug Report Summarization
Haoran Liu,Yue Yu,Shanshan Li,Yong Guo,Deze Wang,Xiaoguang Mao +5 more
- 13 Jul 2020
TL;DR: This paper proposes a novel unsupervised approach based on deep learning network, called BugSum, which integrates an auto-encoder network for feature extraction with a novel metric (believability) to measure the degree to which a sentence is approved or disapproved within discussions.
28
SmartCommit: a graph-based interactive assistant for activity-oriented commits
Bo Shen,Wei Zhang,Christian Kästner,Haiyan Zhao,Zhao Wei,Guangtai Liang,Zhi Jin +6 more
- 20 Aug 2021
TL;DR: SmartCommit as discussed by the authors is a graph-partitioning-based interactive approach to tangled changeset decomposition that leverages not only the efficiency of algorithms but also the knowledge of developers.
21
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