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

基金：

日本学术振兴会;

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

页码：17 / 23

页数：6

共 10 条

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