Practitioners’ Expectations on Log Anomaly Detection

  • Xiaoxue Ma
  • , Yishu Li
  • , Jacky Keung
  • , Xiao Yu
  • , Huiqi Zou
  • , Zhen Yang
  • , Federica Sarro
  • , Earl T. Barr

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish
Pages (from-to)2455-2471
Number of pages17
JournalIEEE Transactions on Software Engineering
Volume51
Issue number9
DOIs
Publication statusPublished - 2025

Keywords

  • Automated log anomaly detection
  • empirical study
  • practitioners’ expectations

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