ALSI-Transformer: Transformer-Based Code Comment Generation With Aligned Lexical and Syntactic Information
Young-Mi Park, Ahjeong Park, Chulyun Kim
Abstract
Code comments explain the operational process of a computer program and increase the long-term productivity of programming tasks such as debugging and maintenance. Therefore, developing methods that automatically generate natural language comments from programming code is required. With the development of deep learning, various excellent models in the natural language processing domain have been applied for comment generation tasks, and recent studies have improved performance by simultaneously using the lexical information of the code token and the syntactical information obtained from the syntax tree. In this paper, to improve the accuracy of automatic comment generation, we introduce a novel syntactic sequence, Code-Aligned Type sequence (CAT), to align the order and length of lexical and syntactic information, and we propose a new neural network model, Aligned Lexical and Syntactic information-Transformer (ALSI-Transformer), based on a transformer that encodes the aligned multi-modal information with convolution and embedding aggregation layers. Through in-depth experiments, we compared ALSI-Transformer with current baseline methods using standard machine translation metrics and demonstrate that the proposed method achieves state-of-the-art performance in code comment generation.