Naming Bug Detection Using Transformer-Based Method Name Suggestion.

被引：0
|
作者：
Minehisa T. [1 ]
Aman H. [2 ]
Kawahara M. [2 ]
机构：
[1] Graduate School of Sc. and Eng., Ehime University
[2] Center for Information Technology, Ehime University
来源：
Computer Software | 2022年 / 39卷 / 04期
关键词：
Bug detection - Code readability - Conventional modeling - Convolutional neural network - Empirical studies - [!text type='Java']Java[!/text] methods - Machine learning models;
D O I：
10.11309/jssst.39.4_17
中图分类号：
学科分类号：
摘要：
The name of a method is an essential clue to comprehending what the method does. An inconsistency between the method’s behavior and its name—especially its first word—is referred to as a “naming bug,” leading to deterioration in the code readability. This paper proposes applying a Transformer-based machine learning model to detect naming bugs in Java methods. The proposed model can evaluate a method name’s consistency by suggesting a proper name from the method’s body. The empirical study proves the proposed model outperforms the conventional model using Doc2Vec, Word2Vec, and convolutional neural network. © 2022 Japan Society for Software Science and Technology. All rights reserved.
引用
收藏
页码：17 / 23
页数：6
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