Background The quest for higher software quality remains a paramount concern in software
testing, prompting a shift towards leveraging machine learning techniques for enhanced …

The rapid development of Machine Learning (ML) has demonstrated superior performance
in many areas, such as computer vision and video and speech recognition. It has now been …

Data errors are a common challenge in machine learning (ML) projects and generally cause
significant performance degradation in ML-enabled software systems. To ensure early …

Background: As Machine Learning (ML) advances rapidly in many fields, it is being adopted
by academics and businesses alike. However, ML has a number of different challenges in …

Robustness of the decision-making system is essential to safe driving, especially under the
environment with inevitable defective information due to the limitations of the perception and …

自动驾驶系统的运行环境复杂多样. 考虑到传感器本身的性能局限及感知算法在特定触发条件下
的功能不足, 自动驾驶系统上游感知结果不可避免地会出现错误. 因此针对自动驾驶决策规划 …

Automated machine learning (AutoML) systems aim at finding the best machine learning
(ML) pipeline that automatically matches the task and data at hand. We investigate the …

For high-level autonomous vehicles, under many circumstances, accidents are not caused
by functional failures, but by system performance limitations and human misuses. ISO 21448 …

Creating resilient machine learning (ML) systems has become necessary to ensure
production-ready ML systems that acquire user confidence seamlessly. The quality of the …

Abstract The traditional software space (1.0) has seen more than fifty years of creation,
testing, and delivery of deterministic software, but this tradition is being disrupted by …