Test Optimization in DNN Testing: A Survey
Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
Abstract
This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.
Topics & Concepts
Computer scienceTest (biology)Software testingReliability engineeringProgramming languageSoftwarePaleontologyEngineeringBiologySoftware Testing and Debugging TechniquesSoftware Engineering ResearchAdversarial Robustness in Machine Learning