Abstract Machine learning has made remarkable advancements, but confidently utilising
learning-enabled components in safety-critical domains still poses challenges. Among the …

Due to the rapid adoption of Deep Neural Networks (DNNs) into larger software systems,
testing of DNN-based systems has received much attention recently. While many different …

With its growing use in safety/security-critical applications, Deep Learning (DL) has raised
increasing concerns regarding its dependability. In particular, DL has a notorious problem of …

Several test adequacy criteria have been developed for quantifying the the coverage of
deep neural networks (DNNs) achieved by a test suite. Being dependent on the structure of …

DNN validation and verification approaches that are input distribution agnostic waste effort
on irrelevant inputs and report false property violations. Drawing on the large body of work …

One of the important certification objectives for airborne software is demonstrating the
absence of unintended behavior. In current software development processes, unintended …

With the use of Deep Learning (DL) in safety-critical domains, the systematic testing of these
systems has become a critical issue for human life. Due to the data-driven nature of Deep …

The increasing use of deep learning (DL) in safety-critical applications highlights the critical
need for systematic and effective testing to ensure system reliability and quality. In this …

Deep neural networks (DNN) are being used in a wide range of applications including safety-
critical systems. Several DNN test generation approaches have been proposed to generate …

Deep neural networks (DNNs) often accept high-dimensional media data (eg, photos, text,
and audio) and understand their perceptual content (eg, a cat). To test DNNs, diverse inputs …