一句话：提出面向自动驾驶汽车系统级测试的充分性度量，以弥补现有度量与故障检测能力相关性弱、依赖模型内部信息的不足。
AI 驱动的系统已广泛应用于自动驾驶汽车等领域，但其复杂特性使可靠性保障十分困难。传统软件测试充分性度量往往不适用于此类系统；针对 AI 系统的白盒度量依赖神经元覆盖等信息，需要访问底层模型和训练数据，而这些数据并不总是可获得。同时，现有充分性度量与测试套件发现故障的能力之间相关性较弱。为弥合这一缺口，本文关注自动驾驶汽车系统级测试的质量评估，提出或研究新的充分性度量。摘要未给出具体方法细节与实验结果，但核心目标是使测试充分性度量更可靠地反映系统级测试发现故障的有效性。
AI-powered systems have gained widespread popularity in various domains, including Autonomous Vehicles (AVs). However, ensuring their reliability and safety is challenging due to their complex nature. Conventional test adequacy metrics, designed to evaluate the effectiveness of traditional software testing, are often insufficient or impractical for these systems. White-box metrics, which are specifically designed for these systems, leverage neuron coverage information. These coverage metrics necessitate access to the underlying AI model and training data, which may not always be available. Furthermore, the existing adequacy metrics exhibit weak correlations with the ability to detect faults in the generated test suite, creating a gap that we aim to bridge in this study.
DOI 原文 ·
@inproceedings{paperbot11257,
title = {Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles},
author = {Neelofar Neelofar and Aldeida Aleti},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3623314}
}