Deep learning (DL) systems can make our life much easier, and thus are gaining more and
more attention from both academia and industry. Meanwhile, bugs in DL systems can be …

Recently, there has been a significant growth of interest in applying software engineering
techniques for the quality assurance of deep learning (DL) systems. One popular direction is …

Deep learning has recently been widely applied to many applications across different
domains, eg, image classification and audio recognition. However, the quality of Deep …

Inspired by the great success of using code coverage as guidance in software testing, a lot
of neural network coverage criteria have been proposed to guide testing of neural network …

Deep neural networks (DNN) have been widely applied in modern life, including critical
domains like autonomous driving, making it essential to ensure the reliability and robustness …

Deep neural networks (DNNs) are widely used in various application domains such as
image processing, speech recognition, and natural language processing. However, testing …

Recent effort to test deep learning systems has produced an intuitive and compelling test
criterion called neuron coverage (NC), which resembles the notion of traditional code …

Deep Neural Networks (DNNs) have been extensively used in many areas including image
processing, medical diagnostics and autonomous driving. However, DNNs can exhibit …

Surprise Adequacy (SA) is one of the emerging and most promising adequacy criteria for
Deep Learning (DL) testing. As an adequacy criterion, it has been used to assess the …

The increasing use of Machine Learning (ML) software can lead to unfair and unethical
decisions, thus fairness bugs in software are becoming a growing concern. Addressing …