5 papers
Improving the Robustness of Large Language Models for Code Tasks via Fine-tuning with Perturbed Data
Yang Liu, Armstrong Foundjem, Xingfang Wu +2
Context: In the fast-paced evolution of software development, Large Language Models (LLMs) have become indispensable tools for tasks such as code generation, completion, analysis,…
Plug it and Play on Logs: A Configuration-Free Statistic-Based Log Parser
Qiaolin Qin, Xingfang Wu, Heng Li +1
Log parsing is an essential task in log analysis, and many tools have been designed to accomplish it. Existing log parsers can be categorized into statistic-based and semantic-base…
SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
Roozbeh Aghili, Xingfang Wu, Foutse Khomh +1
Software logs are messages recorded during the execution of a software system that provide crucial run-time information about events and activities. Although software logs have a c…
LogLSHD: Fast Log Parsing with Locality-Sensitive Hashing and Dynamic Time Warping
Shu-Wei Huang, Xingfang Wu, Heng Li
Large-scale software systems generate vast volumes of system logs that are essential for monitoring, diagnosing, and performance optimization. However, the unstructured nature and…
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach
Xingfang Wu, Heng Li, Foutse Khomh
Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications and systems. State-of-the-art log-based…