一句话：通过挖掘 Stack Overflow 帖子，系统分类深度学习软件部署中的具体挑战并给出行动启示。
深度学习软件（DL software）将大规模数据训练的模型与基于 TensorFlow、Keras 等框架编写的 DL 程序结合，其部署阶段面临新挑战，但已有研究多关注开发过程和程序故障。本文挖掘 Stack Overflow 上 3,023 条相关帖子，并对 769 条样本进行人工检查，以理解 DL 软件部署中的开发者困难。研究发现相关讨论持续增长且难度较高，构建了部署挑战分类体系，并为研究者、开发者和 DL 框架厂商提出可操作的启示。
Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications. These software applications, named as DL based software (in short as DL software), integrate DL models trained using a large data corpus with DL programs written based on DL frameworks such as TensorFlow and Keras. A DL program encodes the network structure of a desirable DL model and the process by which the model is trained using the training data. To help developers of DL software meet the new challenges posed by DL, enormous research efforts in software engineering have been devoted. Existing studies focus on the development of DL software and extensively analyze faults in DL programs. However, the deployment of DL software has not been comprehensively studied. To fill this knowledge gap, this paper presents a comprehensive study on understanding challenges in deploying DL software. We mine and analyze 3,023 relevant posts from Stack Overflow, a popular Q&A website for developers, and show the increasing popularity and high difficulty of DL software deployment among developers. We build a taxonomy of specific challenges encountered by developers in the process of DL software deployment through manual inspection of 769 sampled posts and report a series of actionable implications for researchers, developers, and DL framework vendors.
DOI 原文 ·
@inproceedings{paperbot5839,
title = {A comprehensive study on challenges in deploying deep learning based software},
author = {Zhenpeng Chen and Yanbin Cao and Yuanqiang Liu and Haoyu Wang and Tao Xie and Xuanzhe Liu},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3409759}
}