论文标题
关于跨项目学习与最近的邻居的相关性
On the Relevance of Cross-project Learning with Nearest Neighbours for Commit Message Generation
论文作者
论文摘要
提交消息在软件维护和发展中起着重要作用。但是，开发人员通常不会产生高质量的信息。近年来，已经提出了许多提交消息生成方法来解决此问题。其中一些方法基于神经机器翻译（NMT）技术。研究表明，最近的邻居算法（NNGEN）优于现有的基于NMT的方法，尽管NNGEN比NMT更简单，更快。在本文中，我们表明NNGEN在大多数情况下都没有利用跨项目的学习。我们还表明，现有NNGEN方法的变化更加简单，更快，它在不使用交叉项目的情况下以BLEU_4分数优于BLEU_4分数。
Commit messages play an important role in software maintenance and evolution. Nonetheless, developers often do not produce high-quality messages. A number of commit message generation methods have been proposed in recent years to address this problem. Some of these methods are based on neural machine translation (NMT) techniques. Studies show that the nearest neighbor algorithm (NNGen) outperforms existing NMT-based methods, although NNGen is simpler and faster than NMT. In this paper, we show that NNGen does not take advantage of cross-project learning in the majority of the cases. We also show that there is an even simpler and faster variation of the existing NNGen method which outperforms it in terms of the BLEU_4 score without using cross-project learning.
