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of 1 625
pro vyhledávání: '"search based testing"'
Search-based software testing (SBST) is a widely adopted technique for testing complex systems with large input spaces, such as Deep Learning-enabled (DL-enabled) systems. Many SBST techniques focus on Pareto-based optimization, where multiple object
Externí odkaz:
http://arxiv.org/abs/2410.11769
Akademický článek
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Publikováno v:
EPTCS 389, 2023, pp. 1-10
The safety of the systems controlled by software is a very important area in a digitalized society, as the number of automated processes is increasing. In this paper, we present the results of testing the accuracy of different lane keeping controller
Externí odkaz:
http://arxiv.org/abs/2309.13795
Search-based software testing (SBT) is an effective and efficient approach for testing automated driving systems (ADS). However, testing pipelines for ADS testing are particularly challenging as they involve integrating complex driving simulation pla
Externí odkaz:
http://arxiv.org/abs/2306.10296
Autor:
Nejati, Shiva, Sorokin, Lev, Safin, Damir, Formica, Federico, Mahboob, Mohammad Mahdi, Menghi, Claudio
Surrogate-assisted search-based testing (SA-SBT) aims to reduce the computational time for testing compute-intensive systems. Surrogates enhance testing techniques by improving test case generation focusing the testing budget on the most critical por
Externí odkaz:
http://arxiv.org/abs/2305.00083
Evaluation of deep reinforcement learning (RL) is inherently challenging. Especially the opaqueness of learned policies and the stochastic nature of both agents and environments make testing the behavior of deep RL agents difficult. We present a sear
Externí odkaz:
http://arxiv.org/abs/2205.04887
Akademický článek
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Autor:
Zolfagharian, Amirhossein, Abdellatif, Manel, Briand, Lionel, Bagherzadeh, Mojtaba, S, Ramesh
Publikováno v:
in IEEE Transactions on Software Engineering, vol. 49, no. 7, pp. 3715-3735, July 2023
Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challenges when deployed
Externí odkaz:
http://arxiv.org/abs/2206.07813
Generating a test suite for a quantum program such that it has the maximum number of failing tests is an optimization problem. For such optimization, search-based testing has shown promising results in the context of classical programs. To this end,
Externí odkaz:
http://arxiv.org/abs/2204.08561
Autor:
Moghadam, Mahshid Helali, Borg, Markus, Saadatmand, Mehrdad, Mousavirad, Seyed Jalaleddin, Bohlin, Markus, Lisper, Björn
This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utili
Externí odkaz:
http://arxiv.org/abs/2203.12026