Is using deep learning frameworks free?: Characterizing technical debt in deep learning frameworks

Developers of deep learning applications (shortened as application developers) commonly use deep learning frameworks in their projects. However, due to time pressure, market competition, and cost reduction, developers of deep learning frameworks (shortened as framework developers) often have to sacr...

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Main Authors: LIU, Jiakun, HUANG, Qiao, XIA, Xin, SHIHAB, Emad, LO, David, LI, Shanping
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5645
https://ink.library.smu.edu.sg/context/sis_research/article/6648/viewcontent/liu_icse2020.pdf
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spelling sg-smu-ink.sis_research-66482021-05-11T09:18:08Z Is using deep learning frameworks free?: Characterizing technical debt in deep learning frameworks LIU, Jiakun HUANG, Qiao XIA, Xin SHIHAB, Emad LO, David LI, Shanping Developers of deep learning applications (shortened as application developers) commonly use deep learning frameworks in their projects. However, due to time pressure, market competition, and cost reduction, developers of deep learning frameworks (shortened as framework developers) often have to sacrifice software quality to satisfy a shorter completion time. This practice leads to technical debt in deep learning frameworks, which results in the increasing burden to both the application developers and the framework developers in future development.In this paper, we analyze the comments indicating technical debt (self-admitted technical debt) in 7 of the most popular open-source deep learning frameworks. Although framework developers are aware of such technical debt, typically the application developers are not. We find that: 1) there is a significant number of technical debt in all the studied deep learning frameworks. 2) there is design debt, defect debt, documentation debt, test debt, requirement debt, compatibility debt, and algorithm debt in deep learning frameworks. 3) the majority of the technical debt in deep learning framework is design debt (24.07% - 65.27%), followed by requirement debt (7.09% - 31.48%) and algorithm debt (5.62% - 20.67%). In some projects, compatibility debt accounts for more than 10%. These findings illustrate that technical debt is common in deep learning frameworks, and many types of technical debt also impact the deep learning applications. Based on our findings, we highlight future research directions and provide recommendations for practitioners. 2020-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5645 info:doi/10.1145/3377815.3381377 https://ink.library.smu.edu.sg/context/sis_research/article/6648/viewcontent/liu_icse2020.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software evolution Maintaining software self-admitted technical debt deep learning categorization empirical study Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Software evolution
Maintaining software
self-admitted technical debt
deep learning
categorization
empirical study
Software Engineering
spellingShingle Software evolution
Maintaining software
self-admitted technical debt
deep learning
categorization
empirical study
Software Engineering
LIU, Jiakun
HUANG, Qiao
XIA, Xin
SHIHAB, Emad
LO, David
LI, Shanping
Is using deep learning frameworks free?: Characterizing technical debt in deep learning frameworks
description Developers of deep learning applications (shortened as application developers) commonly use deep learning frameworks in their projects. However, due to time pressure, market competition, and cost reduction, developers of deep learning frameworks (shortened as framework developers) often have to sacrifice software quality to satisfy a shorter completion time. This practice leads to technical debt in deep learning frameworks, which results in the increasing burden to both the application developers and the framework developers in future development.In this paper, we analyze the comments indicating technical debt (self-admitted technical debt) in 7 of the most popular open-source deep learning frameworks. Although framework developers are aware of such technical debt, typically the application developers are not. We find that: 1) there is a significant number of technical debt in all the studied deep learning frameworks. 2) there is design debt, defect debt, documentation debt, test debt, requirement debt, compatibility debt, and algorithm debt in deep learning frameworks. 3) the majority of the technical debt in deep learning framework is design debt (24.07% - 65.27%), followed by requirement debt (7.09% - 31.48%) and algorithm debt (5.62% - 20.67%). In some projects, compatibility debt accounts for more than 10%. These findings illustrate that technical debt is common in deep learning frameworks, and many types of technical debt also impact the deep learning applications. Based on our findings, we highlight future research directions and provide recommendations for practitioners.
format text
author LIU, Jiakun
HUANG, Qiao
XIA, Xin
SHIHAB, Emad
LO, David
LI, Shanping
author_facet LIU, Jiakun
HUANG, Qiao
XIA, Xin
SHIHAB, Emad
LO, David
LI, Shanping
author_sort LIU, Jiakun
title Is using deep learning frameworks free?: Characterizing technical debt in deep learning frameworks
title_short Is using deep learning frameworks free?: Characterizing technical debt in deep learning frameworks
title_full Is using deep learning frameworks free?: Characterizing technical debt in deep learning frameworks
title_fullStr Is using deep learning frameworks free?: Characterizing technical debt in deep learning frameworks
title_full_unstemmed Is using deep learning frameworks free?: Characterizing technical debt in deep learning frameworks
title_sort is using deep learning frameworks free?: characterizing technical debt in deep learning frameworks
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5645
https://ink.library.smu.edu.sg/context/sis_research/article/6648/viewcontent/liu_icse2020.pdf
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