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

The rapid development of artificial intelligence (AI) and breakthroughs in Internet of Things
(IoT) technologies have driven the innovation of advanced autonomous driving systems …

Automatic program understanding and generation techniques could significantly advance
the productivity of programmers and have been widely studied by academia and industry …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

A hard challenge in develo** practical face recognition (FR) attacks is due to the black-
box nature of the target FR model, ie, inaccessible gradient and parameter information to …

Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the
industry and research communities have acknowledged the need for such a pillar by …

Automated Driving Systems (ADS) have made great achievements in recent years thanks to
the efforts from both academia and industry. A typical ADS is composed of multiple modules …

Deep Learning (DL) has gained wide attention in recent years. Meanwhile, bugs in DL
systems can lead to serious consequences, and may even threaten human lives. As a result …

Coverage-guided fuzzing has become mainstream in fuzzing to automatically expose
program vulnerabilities. Recently, a group of fuzzers are proposed to adopt a random search …

Unmanned Aerial Vehicles (UAVs) are nowadays used in a variety of applications. Given the
cyber-physical nature of UAVs, software defects in these systems can cause issues with …