3 citations · 5 across the 9 of their papers we have counts for
11 papers
Large Language Models for Fuzz Testing in Microservices: A Systematic Literature Review
Ying Song, Ke Ping, Yuqing Wang +1
Microservice systems (MSS) increasingly rely on heterogeneous APIs whose combinatorial input space and stateful dependencies challenge traditional fuzz testing. Meanwhile, Large La…
Fault Injection in OpenAPI Specifications for Evaluating Black-Box Testing Effectiveness
Hamza Bin Mazhar, Yuqing Wang, Mika V. Mäntylä
OpenAPI specifications are the primary input for black-box testing tools in microservice systems (MSS), yet prior work shows these specifications are often incomplete, inconsistent…
Towards LLM Accelerated Rapid Reviews for Software Tool Discovery -- Case for Log Anomaly Detection
Jesse Nyyssölä, Hamza Bin Mazhar, Alexander Bakhtin +6
In software engineering research, the primary outcome is frequently a tool. However, for practitioners and academics alike, it is hard to tell which tools are maintained and do the…
LATS-RCA: Language Agent Tree Search for Root Cause Analysis in Microservices
Alexander Naakka, Yuqing Wang, Mika V Mäntylä
Recent advances in large language models (LLMs) have enabled early attempts to automate root cause analysis (RCA) in microservice systems (MSS). However, existing approaches typica…
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…
LO2: Microservice API Anomaly Dataset of Logs and Metrics
Alexander Bakhtin, Jesse Nyyssölä, Yuqing Wang +5
Context. Microservice-based systems have gained significant attention over the past years. A critical factor for understanding and analyzing the behavior of these systems is the co…
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…
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…
LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detector
Mika Mäntylä, Yuqing Wang, Jesse Nyyssölä
This paper introduces LogLead, a tool designed for efficient log analysis benchmarking. LogLead combines three essential steps in log processing: loading, enhancing, and anomaly de…