Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Bernhard K. Aichernig"'
Publikováno v:
Electronic Proceedings in Theoretical Computer Science, Vol 80, Iss Proc. MBT 2012, Pp 88-102 (2012)
Model-based mutation testing uses altered test models to derive test cases that are able to reveal whether a modelled fault has been implemented. This requires conformance checking between the original and the mutated model. This paper presents an ap
Externí odkaz:
https://doaj.org/article/f9393d5f128f4b588ecc14da0559bfb1
Publikováno v:
Innovations in Systems and Software Engineering. 18:417-426
AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for deterministic, non-deterministic, and stochastic systems. We put a special focus on the conformance testing aspect in act
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI 2022) Main Track.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI 2022) Main Track.
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::387d3048439e1c0917a110d7dd48b35f
http://arxiv.org/abs/2205.04887
http://arxiv.org/abs/2205.04887
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031067723
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a433fe6d0add12b2502e8e6704fdfd65
https://doi.org/10.1007/978-3-031-06773-0_26
https://doi.org/10.1007/978-3-031-06773-0_26
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031067723
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad72ae62d3c44fa5faaf46992be8db7f
https://doi.org/10.1007/978-3-031-06773-0_20
https://doi.org/10.1007/978-3-031-06773-0_20
Automata learning is a technique to automatically infer behavioral models of black-box systems. Today's learning algorithms enable the deduction of models that describe complex system properties, e.g., timed or stochastic behavior. Despite recent imp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da5758ec91f439cca1b9289a916638d7
Autor:
Martin Tappler, Bernhard K. Aichernig
Publikováno v:
Formal Methods in System Design. 54:416-448
Model checking has a long-standing tradition in software verification. Given a system design it checks whether desired properties are satisfied. Unlike testing, it cannot be applied in a black-box setting. To overcome this limitation Peled et al. int
Autor:
Robert Korošec, Priska Bauerstätter, Severin Kann, Elisabeth Jöbstl, Rupert Schlick, Richard Schumi, Cristinel Mateis, Bernhard K. Aichernig, Willibald Krenn
Publikováno v:
Software Quality Journal. 27:757-795
Since computers have become increasingly more powerful, users are less willing to accept slow responses of systems. Hence, performance testing is important for interactive systems. However, it is still challenging to test if a system provides accepta
Publikováno v:
ICST
The number of devices in the Internet of Things (IoT) immensely grew in recent years. A frequent challenge in the assurance of the dependability of IoT systems is that components of the system appear as a black box. This paper presents a semi-automat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbb96857ccb9c77dec4f1e08e3b4bdd5
Publikováno v:
Formal Methods ISBN: 9783030908690
FM
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Active automata learning is a technique to automatically infer behavioral models of black-box systems. Today’s learning algorithms enable the deduction of models that describe complex system properties, e.g., timed or stochastic behavior. Despite r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe71217db37fdb7f3d79cfc0e6c85cec
https://doi.org/10.1007/978-3-030-90870-6_28
https://doi.org/10.1007/978-3-030-90870-6_28