Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2022
Abstract
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 studied. Compared with native DL applications, JavaScript-based DL applications can run on major browsers, making the platform- and device-independent. Specifically, the quality of JavaScript-based DL applications depends on the 3 parts: the application, the third-party DL library used and the underlying DL framework (e.g., TensorFlow.js), called JavaScript-based DL system. In this paper, we conduct the first empirical study on the quality issues of JavaScript-based DL systems. Specifically, we collect and analyze 700 real-world faults from relevant GitHub repositories, including the official TensorFlow.js repository, 13 third-party DL libraries, and 58 JavaScript-based DL applications. To better understand the characteristics of these faults, we manually analyze and construct taxonomies for the fault symptoms, root causes, and fix patterns, respectively. Moreover, we also study the fault distributions of symptoms and root causes, in terms of the different stages of the development lifecycle, the 3-level architecture in the DL system, and the 4 major components of TensorFlow.js framework. Based on the results, we suggest actionable implications and research avenues that can potentially facilitate the development, testing, and debugging of JavaScript-based DL systems.
Keywords
JavaScript, Deep Learning, TensorFlow.js, Faults
Discipline
Artificial Intelligence and Robotics | Programming Languages and Compilers
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Oakland Center, Michigan, United States, 2022 October 10-14
First Page
1
Last Page
13
Publisher
ASE
City or Country
United States
Citation
QUAN, Lili; GUO, Qianyu; XIE, Xiaofei; CHEN, Sen; LI, Xiaohong; and LIU, Yang.
Towards understanding the faults of JavaScript-based deep learning systems. (2022). Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Oakland Center, Michigan, United States, 2022 October 10-14. 1-13.
Available at: https://ink.library.smu.edu.sg/sis_research/7715
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Programming Languages and Compilers Commons