  Actionable Warning Identification (AWI) plays a crucial role in improving the
usability of static code analyzers. With recent advances in Machine Learning
(ML), various approaches have been proposed to incorporate ML techniques into
AWI. These ML-based AWI approaches, benefiting from ML's strong ability to
learn subtle and previously unseen patterns from historical data, have
demonstrated superior performance. However, a comprehensive overview of these
approaches is missing, which could hinder researchers/practitioners from
understanding the current process and discovering potential for future
improvement in the ML-based AWI community. In this paper, we systematically
review the state-of-the-art ML-based AWI approaches. First, we employ a
meticulous survey methodology and gather 50 primary studies from 2000/01/01 to
2023/09/01. Then, we outline the typical ML-based AWI workflow, including
warning dataset preparation, preprocessing, AWI model construction, and
evaluation stages. In such a workflow, we categorize ML-based AWI approaches
based on the warning output format. Besides, we analyze the techniques used in
each stage, along with their strengths, weaknesses, and distribution. Finally,
we provide practical research directions for future ML-based AWI approaches,
focusing on aspects like data improvement (e.g., enhancing the warning labeling
strategy) and model exploration (e.g., exploring large language models for
AWI).
