Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Machine learning models are widely used, but can also often be wrong. Users would benefit
from a reliable indication of whether a given output from a given model should be trusted, so …

Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the
quality of DNNs. However, labeling test inputs to check the correctness of DNN prediction is …

Machine learning (ML) cloud APIs enable developers to easily incorporate learning
solutions into software systems. Unfortunately, ML APIs are challenging to use correctly and …

An increasing number of software applications incorporate machine learning (ML) solutions
for cognitive tasks that statistically mimic human behaviors. To test such software …

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 …

Abstract Machine learning models are widely used but can also often be wrong. Users
would benefit from a reliable indication of whether a given output from a given model should …

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 …

The increasing number of software applications incorporating machine learning (ML)
solutions has led to the need for testing techniques. However, testing ML software requires …

Efficiently executing inference tasks of deep neural networks on devices with limited
resources poses a significant load in IoT systems. To alleviate the load, one innovative …