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DoppelTest

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 27 Jan 2023Publisher:Zenodo
Authors: Huai, Yuqi; Chen, Yuntianyi; Almanee, Sumaya; Ngo, Tuan; Liao, Xiang; Wan, Ziwen; Chen, Qi Alfred; +1 Authors

doi: 10.5281/zenodo.7657587 , 10.5281/zenodo.7575582 , 10.5281/zenodo.7575583 , 10.5281/zenodo.7646407 , 10.5281/zenodo.7622184

Abstract

DoppelTest is a Python framework implemented to evaluate a novel autonomous driving software (ADS) testing approach discussed in the paper titled Doppelganger Test Generation for Revealing Bugs in Autonomous Driving Software. This research artifact implements a framework that orchestrates multiple instances of the same ADS and generates virtual scenarios with those instances. Since all vehicles in the virtual scenario are controlled by different instances of the ADS under test, any actual violation that occurs by or among them inherently reveals ADS misbehavior, thus revealing ADS bugs.

Funding: NSF-1823262, NSF-1929771, NSF-1932464, NSF-2145493

Related Organizations
Keywords

Doppelganger Testing, DoppelTest, ICSE 2023

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selected citations
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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).
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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.
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