论文标题
一致的Java方法名称的两相推荐框架
A Two-phase Recommendation Framework for Consistent Java Method Names
论文作者
论文摘要
在软件工程（SE）任务中，命名方法非常重要，以至于它吸引了来自世界各地的许多学者来研究如何提高方法名称的质量。为了准确推荐方法名称，我们使用一个新颖的框架来解决此问题。在我们的经验中，从开源组织中收集了近800万种Java方法作为我们的评估数据集。在第一阶段的建议中，我们基于快速的文本神经网络引入了一个快速，简单的分类器，用于推荐潜在方法类别。在第二阶段推荐中，我们使用两个长期的短期存储网络来指定每个分类中的consit方法名称。评估结果证明，所提出的方法显着优于最先进的方法。
In software engineering (SE) tasks, the naming approach is so important that it attracts many scholars from all over the world to study how to improve the quality of method names. To accurately recommend method names, we employ a novel framework to handle this problem. In our expeirments, nearly 8 million Java methods are collected from open source organizations as our evaluation dataset. In the first-phase recommendation, we introduce a fast and simple classifier based on the fast text neural network for reccomending potential method category. In the second-phase recomendation, we employ both two Long Short Term Memory Networks to reccomend consitent method names from each classification. Evaluation results prove that the proposed approach significantly outperforms state-of-the-art approach.
