  Actionable Warning Identification (AWI) plays a pivotal role in improving the
usability of static code analyzers. Currently, Machine Learning (ML)-based AWI
approaches, which mainly learn an AWI classifier from labeled warnings, are
notably common. However, these approaches still face the problem of restricted
performance due to the direct reliance on a limited number of labeled warnings
to develop a classifier. Very recently, Pre-Trained Models (PTMs), which have
been trained through billions of text/code tokens and demonstrated substantial
success applications on various code-related tasks, could potentially
circumvent the above problem. Nevertheless, the performance of PTMs on AWI has
not been systematically investigated, leaving a gap in understanding their pros
and cons. In this paper, we are the first to explore the feasibility of
applying various PTMs for AWI. By conducting the extensive evaluation on 10K+
SpotBugs warnings from 10 large-scale and open-source projects, we observe that
all studied PTMs are consistently 9.85%~21.12% better than the state-of-the-art
ML-based AWI approaches. Besides, we investigate the impact of three primary
aspects (i.e., data preprocessing, model training, and model prediction) in the
typical PTM-based AWI workflow. Further, we identify the reasons for current
PTMs' underperformance on AWI. Based on our findings, we provide several
practical guidelines to enhance PTM-based AWI in future work.
