arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.00760cs.SE
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关于部署基于深度学习的软件所面临挑战的综合研究

A Comprehensive Study on Challenges in Deploying Deep Learning Based Software

Zhenpeng Chen, Yanbin Cao, Yuanqiang Liu, Haoyu Wang, Tao Xie, Xuanzhe Liu

摘要:

深度学习(DL)变得越来越普遍,被广泛用于各种软件应用程序中。这些软件应用程序被称为基于DL的软件(简称DL软件),将使用大型数据语料库训练的DL模型与基于DL框架(如TensorFlow和Keras)编写的DL程序集成在一起。DL程序编码期望的DL模型的网络结构和使用训练数据训练模型的过程。为了帮助深度学习软件的开发人员迎接深度学习带来的新挑战,人们在软件工程方面投入了大量的研究工作。现有的研究主要集中在深度学习软件的开发上,对深度学习程序中的错误进行了广泛的分析。然而,DL软件的部署还没有得到全面的研究。为了填补这一知识空白,本文提出了一个全面的研究,了解部署DL软件的挑战。我们从开发者常用的问答网站Stack Overflow中挖掘并分析了3,023篇相关帖子,显示了深度学习软件部署在开发者中的日益普及和高难度。我们通过对769个抽样帖子的手动检查,建立了开发人员在DL软件部署过程中遇到的具体挑战的分类,并为研究人员,开发人员和DL框架供应商报告了一系列可操作的影响。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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