@article{MTMT:37253633,
	title = {Automated and logically exhaustive generation of traffic scenarios at road junctions using a multi-level danger definition},
	url = {https://m2.mtmt.hu/api/publication/37253633},
	author = {Babikian, Aren A. and Ficsor, Attila and Semeráth, Oszkár and Mussbacher, Gunter and Varró, Dániel},
	doi = {10.1007/s10270-026-01372-y},
	journal-iso = {SOFTW SYST MODEL},
	journal = {SOFTWARE AND SYSTEMS MODELING},
	unique-id = {37253633},
	issn = {1619-1366},
	abstract = {To ensure their safe use, autonomous driving systems (ADSs) must meet rigorous safety assurance criteria that involve executing maneuvers safely within arbitrary scenarios where other actors perform their intended maneuvers. For that purpose, existing scenario generation approaches optimize search to derive scenarios with high probability of dangerous interactions. In this paper, we hypothesize that at road junctions, potential danger predominantly arises from overlapping paths of individual actors carrying out their designated high-level (abstract) maneuvers. As a step toward ADS safety assurance, we propose an approach to derive an exhaustive set of potentially dangerous logical scenarios at any given road junction, i.e., all permutations of overlapping maneuvers assigned to actors, including the ADS, for a given set of possible maneuvers. From these logical scenarios, we derive concrete-level exact paths that actors must follow to guide simulation-based testing toward potential collisions. We conduct extensive experiments over two realistic road junctions with increasing number of external actors to (1) compare our scenario generation approach to the state-of-the-art Scenic tool and to (2) evaluate the behavior of a state-of-the-art learning-based ADS controller. Results show that (1) our approach outperforms Scenic both in terms of achieved coverage and runtime, and (2) that the ADS-under-test is involved in increasing percentages of unsafe behaviors in simulation, which vary according to abstract scenario properties.},
	year = {2026},
	eissn = {1619-1374},
	orcid-numbers = {Ficsor, Attila/0000-0002-0541-4590; Semeráth, Oszkár/0000-0002-3592-5105; Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:37312880,
	title = {From Constraints to Commands: Graph Pattern Differentiation in 4-Valued First-Order Logic},
	url = {https://m2.mtmt.hu/api/publication/37312880},
	author = {Ficsor, Attila and Papp, Inez Anna and Marussy, Kristóf and Semeráth, Oszkár},
	journal-iso = {J OBJECT TECHNOL},
	journal = {JOURNAL OF OBJECT TECHNOLOGY},
	unique-id = {37312880},
	issn = {1660-1769},
	year = {2026},
	pages = {1-14},
	orcid-numbers = {Ficsor, Attila/0000-0002-0541-4590; Marussy, Kristóf/0000-0002-9135-8256; Semeráth, Oszkár/0000-0002-3592-5105}
}

@inproceedings{MTMT:34872599,
	title = {Refinery: Graph Solver as a Service: Refinement-based Generation and Analysis of Consistent Models},
	url = {https://m2.mtmt.hu/api/publication/34872599},
	author = {Marussy, Kristóf and Ficsor, Attila and Semeráth, Oszkár and Varró, Dániel},
	booktitle = {ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings},
	doi = {10.1145/3639478.3640045},
	unique-id = {34872599},
	abstract = {Various software and systems engineering scenarios rely on the systematic construction of consistent graph models. However, automatically generating a diverse set of consistent graph models for complex domain specifications is challenging. First, the graph generation problem must be specified with mathematical precision. Moreover, graph generation is a computationally complex task, which necessitates specialized logic solvers. Refinery is a novel open-source software framework to automatically synthesize a diverse set of consistent domain-specific graph models. The framework offers an expressive high-level specification language using partial models to succinctly formulate a wide range of graph generation challenges. Moreover, it provides a modern cloud-based architecture for a scalable graph solver as a service, which uses logic reasoning rules to efficiently synthesize a diverse set of solutions to graph generation problems by partial model refinement. Applications include system-level architecture synthesis, test generation for modeling tools or traffic scenario synthesis for autonomous vehicles. Video demonstration: https://youtu.be/Qy-3udNsWsM. Continuously deployed at: https://refinery.services/. © 2024 IEEE Computer Society. All rights reserved.},
	year = {2024},
	pages = {64-68},
	orcid-numbers = {Marussy, Kristóf/0000-0002-9135-8256; Ficsor, Attila/0000-0002-0541-4590; Semeráth, Oszkár/0000-0002-3592-5105; Varró, Dániel/0000-0002-8790-252X}
}

@inproceedings{MTMT:33283704,
	title = {An Initial Performance Analysis of Graph Predicate Evaluation over Partial Models},
	url = {https://m2.mtmt.hu/api/publication/33283704},
	isbn = {9789634218722},
	author = {Ficsor, Attila and Semeráth, Oszkár},
	booktitle = {Proceedings of the 29th Minisymposium of the Department of Measurement and Information Systems Budapest University of Technology and Economics},
	doi = {10.3311/MINISY2022-001},
	unique-id = {33283704},
	year = {2022},
	pages = {1-4},
	orcid-numbers = {Ficsor, Attila/0000-0002-0541-4590; Semeráth, Oszkár/0000-0002-3592-5105}
}

@CONFERENCE{MTMT:33283734,
	title = {Semantic Robustness Testing for Vision-Based Machine Learning Components of Autonomous Cyber-Physical Systems},
	url = {https://m2.mtmt.hu/api/publication/33283734},
	author = {Ficsor, Attila},
	booktitle = {The 13th Conference of PhD Students in Computer Science : June 29 – July 1, 2022 Szeged, Hungary},
	unique-id = {33283734},
	year = {2022},
	pages = {35-39},
	orcid-numbers = {Ficsor, Attila/0000-0002-0541-4590}
}

@CONFERENCE{MTMT:33283785,
	title = {Toolchain for the Construction of Realistic Simulated Urban Environments},
	url = {https://m2.mtmt.hu/api/publication/33283785},
	author = {Ficsor, Attila and Pintér, Balázs},
	booktitle = {The 13th Conference of PhD Students in Computer Science : June 29 – July 1, 2022 Szeged, Hungary},
	unique-id = {33283785},
	year = {2022},
	pages = {40-44},
	orcid-numbers = {Ficsor, Attila/0000-0002-0541-4590}
}