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Taxonomy of real faults in deep learning systems

Authors: Nargiz Humbatova; Gunel Jahangirova; Gabriele Bavota; Vincenzo Riccio; Andrea Stocco; Paolo Tonella;

Taxonomy of real faults in deep learning systems

Abstract

The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance. In this paper we introduce a large taxonomy of faults in deep learning (DL) systems. We have manually analysed 1059 artefacts gathered from GitHub commits and issues of projects that use the most popular DL frameworks (TensorFlow, Keras and PyTorch) and from related Stack Overflow posts. Structured interviews with 20 researchers and practitioners describing the problems they have encountered in their experience have enriched our taxonomy with a variety of additional faults that did not emerge from the other two sources. Our final taxonomy was validated with a survey involving an additional set of 21 developers, confirming that almost all fault categories (13/15) were experienced by at least 50\% of the survey participants.

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Italy
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Fault (geology) Set (abstract data type) Taxonomy (general) geography geography.geographical_feature_category business.industry Deep learning Data science Variety (cybernetics) Structured interview Stack overflow Artificial intelligence business

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Machine Learning (cs.LG), taxonomy, Computer Science - Software Engineering, deep learning; real faults; software testing; taxonomy, deep learning, software testing, Software Engineering (cs.SE), Artificial Intelligence (cs.AI), real faults

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    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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138
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