Tag: machine learning
Machine Learning (ML) deals with how computer programs can learn and improve at performing a specific task when trained on historical data. ML is divided into unsupervised learning (such as clustering and topic modeling) and supervised learning (training a system on annotated examples). ML has been a central part of my research since my PhD studies, and even ended up in my thesis title.
SZZ Unleashed: An Open Implementation of the SZZ Algorithm – Featuring Example Usage in a Study of Just-in-Time Bug Prediction for the Jenkins Project
Machine Learning to Guide Performance Testing: An Autonomous Test Framework
Towards Structured Evaluation of Deep Neural Network Supervisors
Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning
Learning-based Response Time Analysis in Real-Time Embedded Systems: A Simulation-based Approach
Automotive Safety and Machine Learning: Initial Results from a Study on How to Adapt the ISO 26262 Safety Standard
Learning-Based Self-Adaptive Assurance of Timing Properties in a Real-Time Embedded System
