Towards understanding the faults of JavaScript-based deep learning systems

• Main Authors: QUAN, Lili, GUO, Qianyu, XIE, Xiaofei, CHEN, Sen, LI, Xiaohong, LIU, Yang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: JavaScript; Deep Learning; TensorFlow.js; Faults; Artificial Intelligence and Robotics; Programming Languages and Compilers
• Online Access: https://ink.library.smu.edu.sg/sis_research/7715; https://ink.library.smu.edu.sg/context/sis_research/article/8718/viewcontent/2209.04791.pdf
• Institution: Singapore Management University

Quality assurance is of great importance for deep learning (DL) systems, especially when they are applied in safety-critical applications. While quality issues of native DL applications have been extensively analyzed, the issues of JavaScript-based DL applications have never been systematically stud...