Ma, X;
Li, Y;
Keung, J;
Yu, X;
Zou, H;
Yang, Z;
Sarro, F;
(2025)
Practitioners’ Expectations on Log Anomaly Detection.
IEEE Transactions on Software Engineering
pp. 1-17.
10.1109/TSE.2025.3586700.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Practitioners’ Expectations on Log Anomaly Detection |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TSE.2025.3586700 |
| Publisher version: | https://doi.org/10.1109/tse.2025.3586700 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10211855 |
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