In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy
and training efficiency. Hence, multiple identical training runs (eg, identical training data …

Mobile applications play an important role in our daily life, while it still remains a challenge
to guarantee their correctness. Model-based and systematic approaches have been applied …

Web testing has long been recognized as a notoriously difficult task. Even nowadays, web
testing still mainly relies on manual efforts in many cases while automated web testing is still …

Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have
been widely studied, and various defenses were proposed. However, the possibility and …

Large multimodal models (LMMs) have evolved from large language models (LLMs) to
integrate multiple input modalities such as visual inputs. This integration augments the …

Android testing tools generate sequences of input events to exercise the state space of the
app-under-test. Existing search-based techniques systematically evolve a population of …

General game testing relies on the use of human play testers, play test scripting, and prior
knowledge of areas of interest to produce relevant test data. Using deep reinforcement …

Deep Neural Networks (DNNs) have been widely used to perform real-world tasks in cyber-
physical systems such as Autonomous Driving Systems (ADS). Ensuring the correct …