LogLabeler: Towards Effective Acquisition of Log Labels in Industrial Log-Based Analysis

Log-based AIOps is a widely researched topic aiming at reducing the developer burden in system maintenance. Since industrial developers prefer lightweight supervised solutions for log-based AIOps, the strong dependence of these solutions on labeled data creates significant challenges for teams new to building log-based AIOps capabilities, such as high labeling costs, inconsistent annotations, and manual management issues. Log-labeling faces challenges in integrating existing artifacts to reduce labeling costs and manage labels effectively. To the best of our knowledge, no prior research addresses of assisting log-labeling problem. In this article, we propose a new approach called LogLabeler to assist developers in annotating and managing log labels. LogLabeler leverages existing artifacts for initial-label-acquisition, minimizes labeling costs by automatically generating all log labels, and shields developers from manual label management through a human-in-the-loop refinement approach. Evaluations on real-world datasets from Alibaba and open-source datasets show that LogLabeler can effectively supplement log labels, achieving comparable accuracy to existing baselines while operating more efficiently. Furthermore, we demonstrate LogLabeler's practical effectiveness at Alibaba through a case study, highlighting its benefits to developers.