Machine Learning for Actionable Warning Identification: A Comprehensive Survey

• URL: http://arxiv.org/abs/2312.00324v2
• Date: Sun, 06 Oct 2024 08:27:32 GMT
• Title: Machine Learning for Actionable Warning Identification: A Comprehensive Survey
• Authors: Xiuting Ge, Chunrong Fang, Xuanye Li, Weisong Sun, Daoyuan Wu, Juan Zhai, Shangwei Lin, Zhihong Zhao, Yang Liu, Zhenyu Chen,
• Abstract summary: Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static code analyzers. Recent advances in Machine Learning (ML) have been proposed to incorporate ML techniques into AWI. This paper systematically reviews the state-of-the-art ML-based AWI approaches.
• Score: 19.18364564227752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 51 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).

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