5053074
doi
10.5281/zenodo.5053074
oai:zenodo.org:5053074
user-eu
Gunel Jahangirova
Università della Svizzera italiana
Gabriele Bavota
Università della Svizzera italiana
Vincenzo Riccio
Università della Svizzera italiana
Andrea Stocco
Università della Svizzera italiana
Paolo Tonella
Università della Svizzera italiana
Taxonomy of Real Faults in Deep Learning Systems
Nargiz Humbatova
Università della Svizzera italiana
doi:10.1145/3377811.3380395
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>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.</p>
<p> </p>
Zenodo
2019-08-01
info:eu-repo/semantics/report
5053073
user-eu
award_title=Self-assessment Oracles for Anticipatory Testing; award_number=787703; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/787703; funder_id=00k4n6c32; funder_name=European Commission;
1625147293.870791
967173
md5:2f410a1c532d1ad6b57c7e9f89116433
https://zenodo.org/records/5053074/files/TR-Precrime-2019-01.pdf
public
10.1145/3377811.3380395
Is obsoleted by
doi
10.5281/zenodo.5053073
isVersionOf
doi