Reference : Testing Vision-Based Control Systems Using Learnable Evolutionary Algorithms
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/33706
Testing Vision-Based Control Systems Using Learnable Evolutionary Algorithms
English
Ben Abdessalem (helali), Raja [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Nejati, Shiva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Stifter, Thomas [International Electronics & Engineering (IEE), Contern, Luxembourg]
2018
Proceedings of the 40th International Conference on Software Engineering (ICSE 2018)
ACM
Yes
No
International
40th International Conference on Software Engineering
from 27-05-2018 to 03-06-2018
Gothenburg
Sweden
[en] Search-based Software Engineering ; Evolutionary algorithms ; Software Testing ; Automotive Software Systems
[en] Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive vision-based control system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that likely to lead to system failures.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/33706
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems

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