Fault Localization in Deep Learning-based Software: A System-level Approach Article Swipe
Mohammad Mehdi Morovati
,
Amin Nikanjam
,
Foutse Khomh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.08172
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.08172
Over the past decade, Deep Learning (DL) has become an integral part of our daily lives. This surge in DL usage has heightened the need for developing reliable DL software systems. Given that fault localization is a critical task in reliability assessment, researchers have proposed several fault localization techniques for DL-based software, primarily focusing on faults within the DL model. While the DL model is central to DL components, there are other elements that significantly impact the performance of DL components. As a result, fault localization methods that concentrate solely on the DL model overlook a large portion of the system. To address this, we introduce FL4Deep, a system-level fault localization approach considering the entire DL development pipeline to effectively localize faults across the DL-based systems. In an evaluation using 100 faulty DL scripts, FL4Deep outperformed four previous approaches in terms of accuracy for three out of six DL-related faults, including issues related to data (84%), mismatched libraries between training and deployment (100%), and loss function (69%). Additionally, FL4Deep demonstrated superior precision and recall in fault localization for five categories of faults including three mentioned fault types in terms of accuracy, plus insufficient training iteration and activation function.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.08172
- https://arxiv.org/pdf/2411.08172
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404407075
All OpenAlex metadata
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https://openalex.org/W4404407075Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.08172Digital Object Identifier
- Title
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Fault Localization in Deep Learning-based Software: A System-level ApproachWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-12Full publication date if available
- Authors
-
Mohammad Mehdi Morovati, Amin Nikanjam, Foutse KhomhList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.08172Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2411.08172Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.08172Direct OA link when available
- Concepts
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Computer science, Fault (geology), Deep learning, Artificial intelligence, Software, Real-time computing, Software engineering, Geology, Operating system, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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