Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study

Lei, H; Zhang, S; Wang, J; Xiao, G; Liu, Y; Sui, Y

Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study

Lei, H Zhang, S Wang, J Xiao, G Liu, Y Sui, Y

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), 2023, 00, pp. 509-520
Issue Date:: 2023-11-02

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Field	Value	Language
dc.contributor.author	Lei, H
dc.contributor.author	Zhang, S
dc.contributor.author	Wang, J
dc.contributor.author	Xiao, G
dc.contributor.author	Liu, Y
dc.contributor.author	Sui, Y https://orcid.org/0000-0002-9510-6574
dc.date	2023-10-09
dc.date.accessioned	2024-06-07T04:39:41Z
dc.date.available	2024-06-07T04:39:41Z
dc.date.issued	2023-11-02
dc.identifier.citation	2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), 2023, 00, pp. 509-520
dc.identifier.isbn	9798350315943
dc.identifier.issn	1071-9458
dc.identifier.uri	http://hdl.handle.net/10453/179452
dc.description.abstract	Deep learning DL is becoming increasingly important and widely used in our society DL projects are mainly built upon DL frameworks which frequently evolve due to the introduction of new features or bug fixing Consequently compatibility issues are commonly seen in DL projects The compatible framework versions may differ across DL projects i e for a specific framework version one project runs normally while the other crashes even if the client code uses the same framework API Existing studies mainly focus on analyzing the API evolution of Python libraries and the related compatibility issues However the difference in framework version compatibility DFVC among DL projects has rarely been systematically studied In this paper we conduct an empirical study on 90 PyTorch and 50 TensorFlow projects collected from GitHub By upgrading and downgrading the framework versions we obtain compatible versions for each project and further investigate the root causes of the different compatible framework versions across projects We summarize seven root causes Python version absence of using the same breaking API import path parameter third party library resource and API usage constraint We further present six implications based on our empirical findings Our study can facilitate DL practitioners to gain a better understanding of the DFVC among DL projects
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP210101348
dc.relation.ispartof	2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)
dc.relation.ispartof	2023 IEEE 34th International Symposium on Software Reliability Engineering
dc.relation.ispartofseries	Proceedings International Symposium on Software Reliability Engineering
dc.relation.isbasedon	10.1109/issre59848.2023.00076
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Florence, Italy
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-06-07T04:39:39Z
pubs.finish-date	2023-10-12
pubs.publication-status	Published
pubs.start-date	2023-10-09
pubs.volume	00

Abstract:

Deep learning DL is becoming increasingly important and widely used in our society DL projects are mainly built upon DL frameworks which frequently evolve due to the introduction of new features or bug fixing Consequently compatibility issues are commonly seen in DL projects The compatible framework versions may differ across DL projects i e for a specific framework version one project runs normally while the other crashes even if the client code uses the same framework API Existing studies mainly focus on analyzing the API evolution of Python libraries and the related compatibility issues However the difference in framework version compatibility DFVC among DL projects has rarely been systematically studied In this paper we conduct an empirical study on 90 PyTorch and 50 TensorFlow projects collected from GitHub By upgrading and downgrading the framework versions we obtain compatible versions for each project and further investigate the root causes of the different compatible framework versions across projects We summarize seven root causes Python version absence of using the same breaking API import path parameter third party library resource and API usage constraint We further present six implications based on our empirical findings Our study can facilitate DL practitioners to gain a better understanding of the DFVC among DL projects

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http://hdl.handle.net/10453/179452