An empirical study on data distribution-aware test selection for deep learning enhancement

• Main Authors: HU, Qiang, GUO, Yuejun, CORDY, Maxime, XIE, Xiaofei, MA, Lei, PAPADAKIS, Mike, LE TRAON, Yves
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Deep learning testing; test selection; data distribution; Artificial Intelligence and Robotics; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7195; https://ink.library.smu.edu.sg/context/sis_research/article/8198/viewcontent/3511598.pdf
• Institution: Singapore Management University

Similar to traditional software that is constantly under evolution, deep neural networks need to evolve upon the rapid growth of test data for continuous enhancement (e.g., adapting to distribution shift in a new environment for deployment). However, it is labor intensive to manually label all of th...