一句话：提出三步法理解、检测并缓解深度学习系统演化中的回归故障。
深度学习系统在工业领域广泛应用，其演化可能引入回归故障并造成严重后果，因此保障演化过程中的质量至关重要。传统软件中理解回归故障的方法依赖显式逻辑结构，难以直接适用于缺乏此类结构的深度学习系统。为此，论文提出一个三步研究方案：首先通过访谈研究了解深度学习开发者的关注点；然后设计回归故障检测方法；最后在理解故障潜在成因的基础上，给出缓解这些回归故障的途径。该工作面向深度学习系统演化中的质量保障问题，试图连接开发者实践、故障检测与故障缓解。
Deep Learning (DL) is widely used in many industrial domains. The evolution of DL systems may induce regression faults, which may lead to serious consequences. Thus, ensuring the quality of DL systems during their evolution is crucial. Although there are existing approaches for understanding regression faults in traditional software, they are not suitable for DL systems because DL systems lack explicit logic structures. To better prevent regression faults in DL systems, in this work, we propose a three-step approach. First, through an interview study, we aim to understand the concerns of DL developers. Then, we propose methods for detecting regression faults. Finally, we suggest ways to mitigate these faults by understanding their potential causes.