  Developers often write low-quality code comments due to the lack of
programming experience, which can reduce the efficiency of developers program
comprehension. Therefore, developers hope that code comment generation tools
can be developed to illustrate the functionality and purpose of the code.
Recently, researchers mainly model this problem as the neural machine
translation problem and tend to use deep learning-based methods. In this study,
we propose a novel method ComFormer based on Transformer and fusion
method-based hybrid code presentation. Moreover, to alleviate OOV
(out-of-vocabulary) problem and speed up model training, we further utilize the
Byte-BPE algorithm to split identifiers and Sim_SBT method to perform AST
Traversal. We compare ComFormer with seven state-of-the-art baselines from code
comment generation and neural machine translation domains. Comparison results
show the competitiveness of ComFormer in terms of three performance measures.
Moreover, we perform a human study to verify that ComFormer can generate
high-quality comments.
