TEASMA: A Practical Methodology for Test Adequacy Assessment of Deep Neural Networks

Learning Failure-Inducing Models for Testing Software-Defined Networks

Automated anomaly detection for categorical data by repurposing a form filling recommender system

LLM meets ML: Data-efficient Anomaly Detection on Unstable Logs

DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks for Image Analysis

Automated Test Case Repair Using Language Models