Abstract
Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over the past decade, it remains unclear what are practitioners' expectations on log anomaly detection and whether current research meets their needs. To fill this gap, we conduct an empirical study, surveying 312 practitioners from 36 countries about their expectations on log anomaly detection. In particular, we investigate various factors influencing practitioners' willingness to adopt log anomaly detection tools. We then perform a literature review on log anomaly detection, focusing on publications in premier venues from 2015 to 2025, to compare practitioners' needs with the current state of research. Based on this comparison, we highlight the directions for researchers to focus on to develop log anomaly detection techniques that better meet practitioners' expectations. Index Terms-Automated log anomaly detection, empirical study, practitioners' expectations. I. INTRODUCTION L OGS are a crucial source of information in software systems, capturing system runtime behavior and aiding in software engineering tasks like program comprehension [1], [2], anomaly detection [3], [4], [5], [6], and failure diagnosis [7], [8]. Extensive machine learning (ML) and deep learning (DL)-based techniques have been proposed in research for automated log anomaly detection [9], [10], [11], with the aim Received 12 February 2025; revised 4 June 2025; accepted 4 July 2025. Date of publication 8 July 2025; date of current version 19 September 2025. This work obtained ethics approval by the Research Ethics Committee of the Hong Kong Metropolitan University (HE-SF2025/19). Recommended for acceptance by M. R. Lyu. (Corresponding author: Xiao Yu.) Xiaoxue Ma and Yishu Li are with the Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong 999077, China (e-mail: kxma@hkmu.edu.hk; sliy@hkmu.edu.hk). Jacky Keung is with the Department of Computer Science, City University of Hong Kong, Hong Kong 999077, China (e-mail: jacky.keung@ cityu.edu.hk). Xiao Yu is with the Stat