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Generating Commit Messages from Diffs using Pointer-Generator Network

机译:使用指针生成器网络生成来自Diff的提交消息

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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.
机译:源代码存储库中的提交消息是有价值的,但不容易在跟踪问题,报告错误和了解代码时手动生成。最近发表的作品表明,深度神经电机翻译方法绘制了对自动生成提交消息的大大关注。但是,它们无法处理词汇外(OOV)单词,这些单词是代码差异中的基本上的上下文特定标识符,例如类名和方法名称。在本文中,我们提出了一种基于具有指针发生器网络的改进的序列到序列模型的新方法,以将代码差异转换为提交消息。通过搜索具有最高概率的最小标识符集,PTRGNCMSG优于基于神经电机翻译的最近方法,首先启用OOV字的预测。基于GitHub中的前2,000个Java项目的Diffs和手动提交消息的实验结果表明,Ptrgncmsg优于最先进的方法,通过1.02,胭脂-1到4.00和Rouge-L的改进的方法3.78分别。

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