Compatibility Issues in Deep Learning Systems: Problems and Opportunities

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

Compatibility Issues in Deep Learning Systems: Problems and Opportunities

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

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: ESEC/FSE 2023 - Proceedings of the 31st ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023, pp. 476-488
Issue Date:: 2023-11-30

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Field	Value	Language
dc.contributor.author	Wang, J
dc.contributor.author	Xiao, G
dc.contributor.author	Zhang, S
dc.contributor.author	Lei, H
dc.contributor.author	Liu, Y
dc.contributor.author	Sui, Y https://orcid.org/0000-0002-9510-6574
dc.date.accessioned	2024-04-11T22:58:12Z
dc.date.available	2024-04-11T22:58:12Z
dc.date.issued	2023-11-30
dc.identifier.citation	ESEC/FSE 2023 - Proceedings of the 31st ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023, pp. 476-488
dc.identifier.isbn	9798400703270
dc.identifier.uri	http://hdl.handle.net/10453/177780
dc.description.abstract	Deep learning (DL) systems are complex component-based systems, which consist of core program (code implementation and data), Python (language and interpreter), third-party libraries, low-level libraries, development tools, OS, and hardware environments. Incompatible interaction between components would cause serious compatibility issues, substantially affecting the development and deployment processes. What types of compatibility issues are frequently exposed in DL systems? What are the root causes of such issues and how do developers fix them? How far are we from automatically detecting and fixing DL compatibility issues? Although there are many existing studies on DL bugs, the characteristics of DL compatibility issues have rarely been systematically studied and the above questions remain largely unexplored. To fill this gap, we conduct the first comprehensive empirical study to characterize compatibility issues in DL systems. Through analyzing 352 DL compatibility issues classified from 3,072 posts in Stack Overflow, we present their types, manifestation stages, and symptoms. We further summarize the root causes and common fixing strategies, and conduct a tool survey on the current research status of automated detection and repair of DL compatibility issues. Our study allows researchers and practitioners to gain a better understanding of DL compatibility issues and can facilitate future tool development.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/DP210101348
dc.relation.ispartof	ESEC/FSE 2023 - Proceedings of the 31st ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
dc.relation.ispartof	Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
dc.relation.isbasedon	10.1145/3611643.3616321
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Compatibility Issues in Deep Learning Systems: Problems and Opportunities
dc.type	Conference Proceeding
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	2024-04-11T22:58:11Z
pubs.publication-status	Published

Abstract:

Deep learning (DL) systems are complex component-based systems, which consist of core program (code implementation and data), Python (language and interpreter), third-party libraries, low-level libraries, development tools, OS, and hardware environments. Incompatible interaction between components would cause serious compatibility issues, substantially affecting the development and deployment processes. What types of compatibility issues are frequently exposed in DL systems? What are the root causes of such issues and how do developers fix them? How far are we from automatically detecting and fixing DL compatibility issues? Although there are many existing studies on DL bugs, the characteristics of DL compatibility issues have rarely been systematically studied and the above questions remain largely unexplored. To fill this gap, we conduct the first comprehensive empirical study to characterize compatibility issues in DL systems. Through analyzing 352 DL compatibility issues classified from 3,072 posts in Stack Overflow, we present their types, manifestation stages, and symptoms. We further summarize the root causes and common fixing strategies, and conduct a tool survey on the current research status of automated detection and repair of DL compatibility issues. Our study allows researchers and practitioners to gain a better understanding of DL compatibility issues and can facilitate future tool development.

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http://hdl.handle.net/10453/177780