Automated online recognition of unexpected conditions is an indispensable component of
autonomous vehicles to ensure safety even in unknown and uncertain situations. In this …

Assessing the quality of Deep Learning (DL) systems is crucial, as they are increasingly
adopted in safety-critical domains. Researchers have proposed several input generation …

Similar to traditional software that is constantly under evolution, deep neural networks need
to evolve upon the rapid growth of test data for continuous enhancement (eg, adapting to …

Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing
autonomy. With their commercial adoption, the problem of testing their functional and non …

Testing Deep Learning (DL) based systems inherently requires large and representative test
sets to evaluate whether DL systems generalise beyond their training datasets. Diverse Test …

Deep Learning (DL) components are routinely integrated into software systems that need to
perform complex tasks such as image or natural language processing. The adequacy of the …

Software often produces biased outputs. In particular, machine learning (ML) based software
is known to produce erroneous predictions when processing discriminatory inputs. Such …

Deep learning (DL) Systems have been widely used in various domains. Similar to
traditional software, DL system evolution may also incur regression faults. To find the …

Testing deep neural networks (DNNs) has garnered great interest in the recent years due to
their use in many applications. Black-box test adequacy measures are useful for guiding the …

Quality assurance of deep neural networks (DNNs) is crucial for the deployment of DNN-
based software, especially in mission-and safety-critical tasks. Inspired by structural white …