Turker, Uraz; Hierons, Robert; Mousavi, Mohammad Reza; Tyukin, Ivan
Efficient state synchronisation in model-based testing through reinforcement learning Inproceedings
In: Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021)., IEEE/ACM, 2021.
@inproceedings{MousaviASE2021,
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A synchronising sequence is an input sequence that brings a given
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state. Depending on the structure, the system might be complete,
i.e., all inputs are applicable at every state of the system. However,
some systems are partial and in this case not all inputs are
usable at every state. Derivation of synchronising sequences
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paper, we introduce a novel Q-learning algorithm that can derive
synchronising sequences from systems with complete or partial
structures. The proposed algorithm is faster and can process
larger systems than the fastest sequential algorithm that derives
synchronising sequences from complete systems. Moreover, the
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complex systems. Scaling up model-based testing to large systems
requires improving the efficiency of various steps involved in test-
case generation and more importantly, in test-execution. One of
the most costly steps of model-based testing is to bring the system
to a known state, best achieved through synchronising sequences.
A synchronising sequence is an input sequence that brings a given
system to a predetermined state regardless of system’s initial
state. Depending on the structure, the system might be complete,
i.e., all inputs are applicable at every state of the system. However,
some systems are partial and in this case not all inputs are
usable at every state. Derivation of synchronising sequences
from complete or partial systems is a challenging task. In this
paper, we introduce a novel Q-learning algorithm that can derive
synchronising sequences from systems with complete or partial
structures. The proposed algorithm is faster and can process
larger systems than the fastest sequential algorithm that derives
synchronising sequences from complete systems. Moreover, the
proposed method is also faster and can process larger systems
than the most recent massively parallel algorithm that derives
synchronising sequences from partial systems. Furthermore, the
proposed algorithm generates shorter synchronising sequences.
Damasceno, Carlos Diego; Mousavi, Mohammad Reza; Simao, Adenilso
Learning by sampling: learning behavioral family models from software product lines Journal Article
In: Empirical Software Engineering, 26 (4), 2021.
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Fisher, Michael; Mascardi, Viviana; Rozier, Kristin Yvonne; Schlingloff, Bernd-Holger; Winikoff, Michael; Yorke-Smith, Neil
Towards a Framework for Certification of Reliable Autonomous Systems Journal Article
In: Autonomous Agents and Multi Agent Systems, 35 (8), 2021.
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Araujo, Hugo; Hoenselaar, Ties; Mousavi, Mohammad Reza; Vinel, Alex
Connected automated driving: a model-based approach to the analysis of basic awareness services Book Chapter
In: 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1-7, 0000.
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Tyukin, Ivan; Higham, Desmond; Gorban, Alexander
On adversarial examples and stealth attacks in artificial intelligence systems Book Chapter
In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1-6, 0000.
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Miyazawa, Alvaro; Ribeiro, Pedro; Li, Wei; Cavalcanti, Ana; Jon Timmis,; Woodcock, Jim
RoboChart: modelling and verification of the functional behaviour of robotic applications Journal Article
In: Software & Systems Modeling, 18 (5), pp. 3097-3149, 0000.
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Foster, Simon; Baxter, James; Cavalcanti, Ana; Woodcock, Jim; Zeyda, Frank
Unifying Semantic Foundations for Automated Verification Tools in Isabelle/UTP Journal Article
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Louise, Dennis; Fisher, Michael; Lincoln, Nicholas; Lisitsa, Alexei; Veres, Sandor
Practical verification of decision-making in agent-based autonomous systems Journal Article
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Turker, U. C.; Hierons, Robert M.; Mousavi, Mohammad Reza; Tyukin, Ivan Y.
Efficient state synchronisation in model-based testing through reinforcement learning Book Chapter
In: The 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, 0000.
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Damasceno, Carlos Diego Nascimento; Mousavi, Mohammad Reza; Simao, Adenilso Silva
Learning by sampling: learning behavioral family models from software product lines Journal Article
In: Empirical Software Engineering, 26 (1), pp. 1–46, 0000.
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Alves, Gleifer Vaz; Dennis, Louise; Fisher, Michael
A Double-Level Model Checking Approach for an Agent-Based Autonomous Vehicle and Road Junction Regulations Journal Article
In: Journal of Sensor and Actuator Networks, 10 (3), 0000, ISSN: 2224-2708.
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title = {A Double-Level Model Checking Approach for an Agent-Based Autonomous Vehicle and Road Junction Regulations},
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Dennis, Louise A.; Bentzen, Martin Mose; Lindner, Felix; Fisher, Michael
Verifiable Machine Ethics in Changing Contexts Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, 35 (13), pp. 11470-11478, 0000.
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Turker, Uraz; Hierons, Robert; Mousavi, Mohammad Reza; Tyukin, Ivan
Efficient state synchronisation in model-based testing through reinforcement learning Inproceedings
In: Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021)., IEEE/ACM, 2021.
@inproceedings{MousaviASE2021,
title = {Efficient state synchronisation in model-based testing through reinforcement learning},
author = { Turker, Uraz and Hierons, Robert and Mousavi, Mohammad Reza and Tyukin, Ivan },
url = {https://nms.kcl.ac.uk/mohammad.mousavi/pub/mousavi-ase-2021.pdf},
year = {2021},
date = {2021-11-15},
booktitle = {Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021).},
publisher = {IEEE/ACM},
abstract = {Model-based testing is a structured method to test
complex systems. Scaling up model-based testing to large systems
requires improving the efficiency of various steps involved in test-
case generation and more importantly, in test-execution. One of
the most costly steps of model-based testing is to bring the system
to a known state, best achieved through synchronising sequences.
A synchronising sequence is an input sequence that brings a given
system to a predetermined state regardless of system’s initial
state. Depending on the structure, the system might be complete,
i.e., all inputs are applicable at every state of the system. However,
some systems are partial and in this case not all inputs are
usable at every state. Derivation of synchronising sequences
from complete or partial systems is a challenging task. In this
paper, we introduce a novel Q-learning algorithm that can derive
synchronising sequences from systems with complete or partial
structures. The proposed algorithm is faster and can process
larger systems than the fastest sequential algorithm that derives
synchronising sequences from complete systems. Moreover, the
proposed method is also faster and can process larger systems
than the most recent massively parallel algorithm that derives
synchronising sequences from partial systems. Furthermore, the
proposed algorithm generates shorter synchronising sequences.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
complex systems. Scaling up model-based testing to large systems
requires improving the efficiency of various steps involved in test-
case generation and more importantly, in test-execution. One of
the most costly steps of model-based testing is to bring the system
to a known state, best achieved through synchronising sequences.
A synchronising sequence is an input sequence that brings a given
system to a predetermined state regardless of system’s initial
state. Depending on the structure, the system might be complete,
i.e., all inputs are applicable at every state of the system. However,
some systems are partial and in this case not all inputs are
usable at every state. Derivation of synchronising sequences
from complete or partial systems is a challenging task. In this
paper, we introduce a novel Q-learning algorithm that can derive
synchronising sequences from systems with complete or partial
structures. The proposed algorithm is faster and can process
larger systems than the fastest sequential algorithm that derives
synchronising sequences from complete systems. Moreover, the
proposed method is also faster and can process larger systems
than the most recent massively parallel algorithm that derives
synchronising sequences from partial systems. Furthermore, the
proposed algorithm generates shorter synchronising sequences.
Damasceno, Carlos Diego; Mousavi, Mohammad Reza; Simao, Adenilso
Learning by sampling: learning behavioral family models from software product lines Journal Article
In: Empirical Software Engineering, 26 (4), 2021.
@article{Diego2021,
title = {Learning by sampling: learning behavioral family models from software product lines},
author = {Damasceno, Carlos Diego and Mousavi, Mohammad Reza and Simao, Adenilso},
year = {2021},
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Fisher, Michael; Mascardi, Viviana; Rozier, Kristin Yvonne; Schlingloff, Bernd-Holger; Winikoff, Michael; Yorke-Smith, Neil
Towards a Framework for Certification of Reliable Autonomous Systems Journal Article
In: Autonomous Agents and Multi Agent Systems, 35 (8), 2021.
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Araujo, Hugo; Hoenselaar, Ties; Mousavi, Mohammad Reza; Vinel, Alex
Connected automated driving: a model-based approach to the analysis of basic awareness services Book Chapter
In: 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1-7, 0000.
@inbook{Araujo2020,
title = {Connected automated driving: a model-based approach to the analysis of basic awareness services},
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Tyukin, Ivan; Higham, Desmond; Gorban, Alexander
On adversarial examples and stealth attacks in artificial intelligence systems Book Chapter
In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1-6, 0000.
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Miyazawa, Alvaro; Ribeiro, Pedro; Li, Wei; Cavalcanti, Ana; Jon Timmis,; Woodcock, Jim
RoboChart: modelling and verification of the functional behaviour of robotic applications Journal Article
In: Software & Systems Modeling, 18 (5), pp. 3097-3149, 0000.
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Foster, Simon; Baxter, James; Cavalcanti, Ana; Woodcock, Jim; Zeyda, Frank
Unifying Semantic Foundations for Automated Verification Tools in Isabelle/UTP Journal Article
In: CoRR, abs/1905.05500 , 0000.
@article{FosterBCWoodcockZ2019,
title = {Unifying Semantic Foundations for Automated Verification Tools in Isabelle/UTP},
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Louise, Dennis; Fisher, Michael; Lincoln, Nicholas; Lisitsa, Alexei; Veres, Sandor
Practical verification of decision-making in agent-based autonomous systems Journal Article
In: Automated Software Engineering, 23 (3), pp. 305–359, 0000, ISBN: 1573-7535.
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title = {Practical verification of decision-making in agent-based autonomous systems},
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Turker, U. C.; Hierons, Robert M.; Mousavi, Mohammad Reza; Tyukin, Ivan Y.
Efficient state synchronisation in model-based testing through reinforcement learning Book Chapter
In: The 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, 0000.
@inbook{TurkerEfficient2021,
title = {Efficient state synchronisation in model-based testing through reinforcement learning},
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Damasceno, Carlos Diego Nascimento; Mousavi, Mohammad Reza; Simao, Adenilso Silva
Learning by sampling: learning behavioral family models from software product lines Journal Article
In: Empirical Software Engineering, 26 (1), pp. 1–46, 0000.
@article{damasceno2021learning,
title = {Learning by sampling: learning behavioral family models from software product lines},
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Alves, Gleifer Vaz; Dennis, Louise; Fisher, Michael
A Double-Level Model Checking Approach for an Agent-Based Autonomous Vehicle and Road Junction Regulations Journal Article
In: Journal of Sensor and Actuator Networks, 10 (3), 0000, ISSN: 2224-2708.
@article{jsan10030041,
title = {A Double-Level Model Checking Approach for an Agent-Based Autonomous Vehicle and Road Junction Regulations},
author = {Gleifer Vaz Alves and Louise Dennis and Michael Fisher},
url = {https://www.mdpi.com/2224-2708/10/3/41},
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issn = {2224-2708},
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Dennis, Louise A.; Bentzen, Martin Mose; Lindner, Felix; Fisher, Michael
Verifiable Machine Ethics in Changing Contexts Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, 35 (13), pp. 11470-11478, 0000.
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title = {Verifiable Machine Ethics in Changing Contexts},
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