[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124850-en":3,"doc-seo-124850-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124850,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning for Actionable Warning Identification - A Comprehensive Survey","Actionable Warning Identification (AWI) strengthens static code analyzers by turning raw alerts into warnings that developers can act on. This comprehensive survey reviews ML-based AWI approaches and addresses the current gap in an end-to-end understanding of the field. Using a meticulous methodology, the work collects 51 primary studies from 2000/01/01 to 2023/09/01, builds a typical ML-based AWI workflow, and analyzes techniques across dataset preparation, preprocessing, model construction, and evaluation. It also summarizes strengths, weaknesses, distribution, and proposes practical future research directions.","arXiv :2312 .00324v2 [ cs . SE] 6 Oct 2024  \nMachine Learning for Actionable Warning Identification: A Comprehensive Survey  \nXIUTING GE, State Key Laboratory for Novel Software Technology, Nanjing University, China CHUNRONG FANG∗ , State Key Laboratory for Novel Software Technology, Nanjing University, China XUANYE LI, State Key Laboratory for Novel Software Technology, Nanjing University, China WEISONG SUN, College of Computing and Data Science, Nanyang Technological University, Singapore DAOYUAN WU, The Hong Kong University of Science and Technology, China  \nJUAN ZHAI, Manning College of Information & Computer Sciences, University of Massachusetts, USA SHANGWEI LIN, College of Computing and Data Science, Nanyang Technological University, Singapore ZHIHONG ZHAO, State Key Laboratory for Novel Software Technology, Nanjing University, China YANG LIU, College of Computing and Data Science, Nanyang Technological University, Singapore ZHENYU CHEN∗ , State Key Laboratory for Novel Software Technology, Nanjing University, China  \nActionable 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 and 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 a 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 key techniques used in each stage, along with their strengths, weaknesses, and distribution. Finally, we provide practical research directions for future  \n∗ Chunrong Fang and Zhenyu Chen are the corresponding authors.  \nAuthors’ addresses: Xiuting Ge, State Key Laboratory for Novel Software Technology, Nanjing University, No.22 Hankou Road, Nanjing, Jiangsu, China, 210093, [dg20320002@smail.nju.edu.cn](dg20320002@smail.nju.edu.cn); Chunrong Fang, State Key Laboratory for Novel Software Technology, Nanjing University, No.22 Hankou Road, Nanjing, Jiangsu, China, 210093, [fangchunrong@nju.edu.cn](fangchunrong@nju.edu.cn); Xuanye Li, State Key Laboratory for Novel Software Technology, Nanjing University, No.22 Hankou Road, Nanjing, Jiangsu, China, 210093, [1525135604@qq.com](1525135604@qq.com); Weisong Sun, College of Computing and Data Science, Nanyang Technological University, Nanyang Avenue 50, Singapore, [weisong.sun@ntu.edu.sg](weisong.sun@ntu.edu.sg); Daoyuan Wu, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China, [daoyuan@cse.ust.hk](daoyuan@cse.ust.hk); Juan Zhai, Manning College of Information & Computer Sciences, University of Massachusetts, 140 Governors Drive, Amherst, Miami, USA, [juanzhai@umass.edu](juanzhai@umass.edu); Shangwei Lin, College of Computing and Data Science, Nanyang Technological University, Nanyang Avenue 50, Singapore, [shang-wei.lin@ntu.edu.sg](shang-wei.lin@ntu.edu.sg); Zhihong Zhao, State Key Laboratory for Novel Software Technology, Nanjing University, No.22 Hankou Road, Nanjing, Jiangsu, China, 210093, [zhaozhih@nju.edu.cn](zhaozhih@nju.edu.cn); Yang Liu, College of Computing and Data Science, Nanyang Technological University, Nanyang Avenue 50, Singapore, [yangliu@ntu.edu.sg](yangliu@ntu.edu.sg); Zhenyu Chen, State Key Laboratory for Novel Sof","cbCaiegyvAphzv8C","https://ap.wps.com/l/cbCaiegyvAphzv8C","pdf",1656971,1,35,"English","en",105,"# Introduction\n## Actionable Warning Identification and the Role of Machine Learning\n## Survey Methodology and Study Selection\n## Typical ML-Based AWI Workflow\n## Categorization by Warning Output Format\n## Analysis of Key Techniques Across Workflow Stages\n## Future Research Directions","[{\"question\":\"What problem does Actionable Warning Identification (AWI) address?\",\"answer\":\"AWI improves the usability of static code analyzers by helping convert static analysis alerts into actionable warnings for developers.\"},{\"question\":\"How was the survey conducted and how many studies were included?\",\"answer\":\"The survey uses a meticulous methodology and gathers 51 primary studies published between 2000/01/01 and 2023/09/01.\"},{\"question\":\"What does the survey cover in a typical ML-based AWI workflow?\",\"answer\":\"It outlines warning dataset preparation, preprocessing, AWI model construction, and evaluation stages, and analyzes key techniques and their strengths, weaknesses, and distribution.\"}]","Machine Learning for Actionable Warning Identification - 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