摘要
深度学习系统具有强大的学习与推理能力,在无人驾驶、语音识别和机器人等领域应用广泛.由于数据集的限制以及依赖人工标签数据,深度学习系统易于出现非预期的行为.近年来,深度学习系统的质量问题受到广泛的关注,特别是在安全攸关的领域.由于模糊测试具有较强的故障揭示能力,运用模糊测试技术对深度学习系统进行测试成为研究热点.从测试用例生成(包括种子队列构建、种子选择和种子变异)、测试结果判定、覆盖分析3个方面对已有的深度学习系统的模糊测试技术进行总结,并介绍常用的数据集以及度量指标,最后对其发展方向进行展望.
Deep learning(DL)systems have powerful learning and reasoning capabilities and are widely employed in many fields including unmanned vehicles,speech recognition,intelligent robotics,etc.Due to the dataset limit and dependence on manually labeled data,DL systems are prone to unexpected behaviors.Accordingly,the quality of DL systems has received widespread attention in recent years,especially in safety-critical fields.Fuzz testing with strong fault-detecting ability is utilized to test DL systems,which becomes a research hotspot.This study summarizes existing fuzz testing for DL systems in the aspects of test case generation(including seed queue construction,seed selection,and seed mutation),test result determination,and coverage analysis.Additionally,commonly used datasets and metrics are introduced.Finally,the study prospects for the future development of this field.
作者
代贺鹏
孙昌爱
金慧
肖明俊
DAI He-Peng;SUN Chang-Ai;JIN Hui;XIAO Ming-Jun(School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China)
出处
《软件学报》
EI
CSCD
北大核心
2023年第11期5008-5028,共21页
Journal of Software
基金
国家自然科学基金(61872039)
北京市自然科学基金(4162040)
航空科学基金(2016ZD74004)。
关键词
深度学习系统
模糊测试
研究进展
deep learning(DL)system
fuzz testing
state-of-the-art survey
