6 papers
Assessing REST API Test Generation Strategies with Log Coverage
Nana Reinikainen, Mika Mäntylä, Yuqing Wang
Assessing the effectiveness of REST API tests in black-box settings can be challenging due to the lack of access to source code coverage metrics and polyglot tech stack. We propose…
A Comparative Study of Semantic Log Representations for Software Log-based Anomaly Detection
Yuqing Wang, Ying Song, Xiaozhou Li +2
Recent deep learning (DL) methods for log anomaly detection increasingly rely on semantic log representation methods that convert the textual content of log events into vector embe…
AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems
Ke Ping, Hamza Bin Mazhar, Yuqing Wang +2
Microservice systems (MSS) have become a predominant architectural style for cloud services. Yet the community still lacks high-quality, publicly available datasets for anomaly det…
Token Interdependency Parsing (Tipping) -- Fast and Accurate Log Parsing
Shayan Hashemi, Mika Mäntylä
In the last decade, an impressive increase in software adaptions has led to a surge in log data production, making manual log analysis impractical and establishing the necessity fo…
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning
Yuqing Wang, Mika V. Mäntylä, Serge Demeyer +3
Microservice-based systems (MSS) may fail with various fault types. While existing AIOps methods excel at detecting abnormal traces and locating the responsible service(s), human e…
Cross-System Software Log-based Anomaly Detection Using Meta-Learning
Yuqing Wang, Mika V. Mäntylä, Jesse Nyyssölä +2
Modern software systems produce vast amounts of logs, serving as an essential resource for anomaly detection. Artificial Intelligence for IT Operations (AIOps) tools have been deve…