Self-driving cars are on the horizon, making it necessary to consider safety assurance and
homologation of these autonomously operating vehicles. In this study, we systematically …

Combinatorial testing has rapidly gained favor among software testers in the past decade as
improved algorithms have become available and practical success has been demonstrated …

A key challenge of the development and maintenanceof configurable systems is to predict
the performance ofindividual system variants based on the features selected. It isusually …

We develop an approach to machine learning and anomaly detection via quantum adiabatic
evolution. This approach consists of two quantum phases, with some amount of classical …

The rise of highly configurable complex software and its widespread usage requires design
of efficient testing methodology. t-wise coverage is a leading metric to measure the quality of …

The application of meta-heuristic algorithms for t-way testing has recently become prevalent.
Consequently, many useful meta-heuristic algorithms have been developed on the basis of …

Recent advancements in the field of deep learning have enabled its application in
Autonomous Driving Systems (ADS). A Deep Neural Network (DNN) model is often used to …

This paper defines a set difference metric for comparing machine learning (ML) datasets and
proposes the difference between datasets be a function of combinatorial coverage. We …

This paper aims to contribute to the comprehensive and systematic safety assessment of
Automated Driving Systems (ADSs) by identifying unknown hazardous areas of operation …

Machine learning (ML)-based software systems are rapidly gaining adoption across various
domains, making it increasingly essential to ensure they perform as intended. This report …