API misuses often lead to software bugs, crashes, and vulnerabilities. While
several API misuse detectors have been proposed, there are no automatic repair
tools specifically designed for this purpose. In a recent study,
test-suite-based automatic program repair (APR) tools were found to be
ineffective in repairing API misuses. Still, since the study focused on
non-learning-aided APR tools, it remains unknown whether learning-aided APR
tools are capable of fixing API misuses. In recent years, pre-trained language
models (PLMs) have succeeded greatly in many natural language processing tasks.
There is a rising interest in applying PLMs to APR. However, there has not been
any study that investigates the effectiveness of PLMs in repairing API misuse.
To fill this gap, we conduct a comprehensive empirical study on 11
learning-aided APR tools, which include 9 of the state-of-the-art
general-purpose PLMs and two APR tools. We evaluate these models with an
API-misuse repair dataset, consisting of two variants. Our results show that
PLMs perform better than the studied APR tools in repairing API misuses. Among
the 9 pre-trained models tested, CodeT5 is the best performer in the exact
match. We also offer insights and potential exploration directions for future
research.Comment: Under review by TOSE