基于草图的Diffs提交消息生成神经模型
A Sketch-Based Neural Model for Generating Commit Messages from Diffs
摘要：
提交消息在软件开发中有着重要的影响，特别是在大型团队中工作时。具有不同写作风格的多个开发人员可能经常参与同一个项目。出于这个原因，可能很难保持编写信息性提交消息的严格模式，最常见的问题是这些消息的描述性不够。在本文中，我们应用神经机器翻译（NMT）技术，将代码差异转换为提交消息，我们提出了一个改进的基于草图的编码器，这项任务。我们将方法分为三个部分。首先，我们专注于找到一个更合适的NMT基线这个问题。其次，我们证明了NMT模型的性能可以通过对包含特定文件类型的示例进行训练来提高。最后，我们介绍了一种新的基于草图的神经模型的灵感来自最近的方法用于代码生成，我们表明，基于草图的编码器显着优于现有的最先进的解决方案。结果突出表明，这种改进是相关的，特别是Java源代码文件，通过检查两个不同的数据集，近年来介绍了这项任务。
英文摘要：
Commit messages have an important impact in software development, especially when working in large teams. Multiple developers who have a different style of writing may often be involved in the same project. For this reason, it may be difficult to maintain a strict pattern of writing informative commit messages, with the most frequent issue being that these messages are not descriptive enough. In this paper we apply neural machine translation (NMT) techniques to convert code diffs into commit messages and we present an improved sketch-based encoder for this task. We split the approach into three parts. Firstly, we focus on finding a more suitable NMT baseline for this problem. Secondly, we show that the performance of the NMT models can be improved by training on examples containing a specific file type. Lastly, we introduce a novel sketch-based neural model inspired by recent approaches used for code generation and we show that the sketch-based encoder significantly outperforms existing state of the art solutions. The results highlight that this improvement is relevant especially for Java source code files, by examining two different datasets introduced in recent years for this task.
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