Naming Bug Detection Using Transformer-Based Method Name Suggestion

Abstract

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.