  APIs (Application Programming Interfaces) are reusable software libraries and
are building blocks for modern rapid software development. Previous research
shows that programmers frequently share and search for reviews of APIs on the
mainstream software question and answer (Q&A) platforms like Stack Overflow,
which motivates researchers to design tasks and approaches related to process
API reviews automatically. Among these tasks, classifying API reviews into
different aspects (e.g., performance or security), which is called the
aspect-based API review classification, is of great importance. The current
state-of-the-art (SOTA) solution to this task is based on the traditional
machine learning algorithm. Inspired by the great success achieved by
pre-trained models on many software engineering tasks, this study fine-tunes
six pre-trained models for the aspect-based API review classification task and
compares them with the current SOTA solution on an API review benchmark
collected by Uddin et al. The investigated models include four models (BERT,
RoBERTa, ALBERT and XLNet) that are pre-trained on natural languages,
BERTOverflow that is pre-trained on text corpus extracted from posts on Stack
Overflow, and CosSensBERT that is designed for handling imbalanced data. The
results show that all the six fine-tuned models outperform the traditional
machine learning-based tool. More specifically, the improvement on the F1-score
ranges from 21.0% to 30.2%. We also find that BERTOverflow, a model pre-trained
on the corpus from Stack Overflow, does not show better performance than BERT.
The result also suggests that CosSensBERT also does not exhibit better
performance than BERT in terms of F1, but it is still worthy of being
considered as it achieves better performance on MCC and AUC.
