LogUpdater: Automated Detection and Repair of Specific Defects in Logging Statements
This program is tentative and subject to change.
Developers write logging statements to monitor software runtime behaviors and system state. However, poorly constructed or misleading log messages can inadvertently obfuscate actual program execution patterns, thereby impeding effective software maintenance. Existing research on analyzing issues within logging statements is limited, primarily focusing on detecting a singular type of defect and relying on manual intervention for fixes rather than automated solutions.
To address the limitation, we initiate a systematic study that pinpoints four specific types of defects in logging statements (i.e., statement code inconsistency, static dynamic inconsistency, temporal relation inconsistency, and readability issues) through the analysis of real-world log-centric changes. We then propose LogUpdater, a two-stage framework for automatically detecting and updating logging statements for these specific defects. In the offline stage, LogUpdater constructs a similarity-based classifier on a set of synthetic defective logging statements to identify specific defect types. During the online testing phase, this classifier first evaluates logging statements in a given code snippet to determine the necessity and type of improvements required. Then, LogUpdater constructs type-aware prompts from historical logging update changes for an LLM-based recommendation framework to suggest updates addressing these specific defects.
We evaluate the effectiveness of LogUpdater on a dataset containing real-world logging changes, a synthetic dataset, and a new real-world project dataset. The results indicate that our approach is highly effective in detecting logging defects, achieving an F1 score of 0.625. Additionally, it exhibits significant improvements in suggesting precise static text and dynamic variables, with enhancements of 48.12% and 24.90%, respectively. Furthermore, LogUpdater achieves a 61.49% success rate in recommending correct updates on new real-world projects. We reported 40 problematic logging statements and their fixes to GitHub via pull requests, resulting in 25 changes confirmed and merged across 11 different projects.
This program is tentative and subject to change.
Mon 17 NovDisplayed time zone: Seoul change
11:00 - 12:30 | Log & Dependency 1Research Papers / Journal-First Track at Grand Hall 5 Chair(s): Yintong Huo Singapore Management University, Singapore | ||
11:00 10mTalk | LogMoE: Lightweight Expert Mixture for Cross-System Log Anomaly Detection Research Papers Jiaxing Qi Beihang University, Zhongzhi Luan Beihang University, Shaohan Huang Beihang University, Carol Fung Concordia University, Yuchen Wang Beihang University, Aibin Wang Beihang University, Hongyu Zhang Chongqing University, Hailong Yang Beihang University, China, Depei Qian Beihang University, China | ||
11:10 10mTalk | Improving LLM-based Log Parsing by Learning from Errors in Reasoning Traces Research Papers Wang Jialai National University of Singapore, Juncheng Lu Southeast University, Jie Yang Wuhan University, Junjie Wang Institute of Software at Chinese Academy of Sciences, Zeyu Gao Tsinghua University, Chao Zhang Tsinghua University, Zhenkai Liang NUS, Ee-Chien Chang School of Computing, NUS | ||
11:20 10mTalk | LogUpdater: Automated Detection and Repair of Specific Defects in Logging Statements Journal-First Track Renyi Zhong The Chinese University of Hong Kong, Yichen LI ByteDance, Jinxi Kuang The Chinese University of Hong Kong, Wenwei Gu The Chinese University of Hong Kong, Yintong Huo Singapore Management University, Singapore, Michael Lyu The Chinese University of Hong Kong | ||
11:30 10mTalk | LogAction: Consistent Cross-system Anomaly Detection through Logs via Active Domain Adaptation Research Papers Chiming Duan Peking University, Minghua He Peking University, Pei Xiao Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Xin Zhang Peking University, Zhewei Zhong Bytedance, Xiang Luo Bytedance, Yan Niu Bytedance, Lingzhe Zhang Peking University, China, Yifan Wu Peking University, Siyu Yu The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Weijie Hong Peking university, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University | ||
11:40 10mTalk | Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning Research Papers Xin Wang Changsha University of Science and Technology, Zhenhao Li York University, Zishuo Ding The Hong Kong University of Science and Technology (Guangzhou) | ||
11:50 10mTalk | Which Is Better For Reducing Outdated And Vulnerable Dependencies: Pinning Or Floating? Research Papers Imranur Rahman North Carolina State University, Jill Marley North Carolina State University, William Enck North Carolina State University, Laurie Williams North Carolina State University | ||
12:00 10mTalk | On Automating Configuration Dependency Validation via Retrieval-Augmented Generation Research Papers Sebastian Simon Leipzig University, Alina Mailach Leipzig University, Johannes Dorn Leipzig University, Norbert Siegmund Leipzig University Pre-print | ||
12:10 10mTalk | CollaborLog: Efficient-Generalizable Log Anomaly Detection via Large-Small Model Collaboration in Software Evolution Research Papers Pei Xiao Peking University, Chiming Duan Peking University, Minghua He Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Yifan Wu Peking University, Jing Xu ByteDance, Gege Gao ByteDance, Lingzhe Zhang Peking University, China, Weijie Hong Peking university, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University | ||