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\n  \n 2024\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Refinery: Graph Solver as a Service.\n \n \n \n \n\n\n \n Marussy, K.; Ficsor, A.; Semeráth, O.; and Varró, D.\n\n\n \n\n\n\n In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering: Demonstrations, 2024. \n (Accepted)\n\n\n\n
\n\n\n\n \n \n \"Refinery: pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{icse2024,\n    author = {Marussy, Krist\\'of and Ficsor, Attila and Semeráth, Oszk\\'ar and Varr\\'o, D\\'aniel},\n    title = {Refinery: Graph Solver as a Service},\n    year = {2024},\n    booktitle = {Proceedings of the IEEE/ACM 46th International Conference on Software Engineering: Demonstrations},\n    note = {(Accepted)},\n    type = {Conference},\n\n\turl_pdf = {publications/icse24.pdf}, \n\tabstract = {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.},\n}\n
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\n 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.\n
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\n  \n 2022\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n An Initial Performance Analysis of Graph Predicate Evaluation over Partial Models.\n \n \n \n \n\n\n \n Ficsor, A.; and Semeráth, O.\n\n\n \n\n\n\n In Proceedings of the 29th Minisymposium of the Department of Measurement and Information Systems Budapest University of Technology and Economics, pages 1-4, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"An pdf\n  \n \n \n \"An mtmt\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{MTMT:33283704,\n\ttitle = {An Initial Performance Analysis of Graph Predicate Evaluation over Partial Models},\n\tauthor = {Ficsor, Attila and Semeráth, Oszkár},\n\tbooktitle = {Proceedings of the 29th Minisymposium of the Department of Measurement and Information Systems Budapest University of Technology and Economics},\n\tdoi = {10.3311/MINISY2022-001},\n\tunique-id = {33283704},\n\tyear = {2022},\n\tpages = {1-4},\n\torcid-numbers = {Ficsor, Attila/0000-0002-0541-4590},\n\n\turl_pdf = {publications/minisy22.pdf}, \n\turl_mtmt = {https://m2.mtmt.hu/api/publication/33283704},\n\tabstract = {Graph-based modeling tools are widely used during the design, analysis and verification of complex critical systems. Those tools enables the automation of several design steps (e.g., by model transformation), and the early analysis of system designs (e.g. by test generation). The evaluation of complex graph predicates (or graph pattern matching) is a core technique in modeling and model transformation, and essential in scalable graph generation. This motivated the integration of industrial graph pattern matching tools directly to advanced data structures used in model checking and logic reasoning algorithms. In this paper we provide a report of a preliminary performance benchmark combining the incremental graph pattern matching algorithm of the Viatra framework with hash tries used for state space exploration on partial models.},\n}\n\n
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\n Graph-based modeling tools are widely used during the design, analysis and verification of complex critical systems. Those tools enables the automation of several design steps (e.g., by model transformation), and the early analysis of system designs (e.g. by test generation). The evaluation of complex graph predicates (or graph pattern matching) is a core technique in modeling and model transformation, and essential in scalable graph generation. This motivated the integration of industrial graph pattern matching tools directly to advanced data structures used in model checking and logic reasoning algorithms. In this paper we provide a report of a preliminary performance benchmark combining the incremental graph pattern matching algorithm of the Viatra framework with hash tries used for state space exploration on partial models.\n
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\n \n\n \n \n \n \n \n \n Toolchain for the Construction of Realistic Simulated Urban Environments.\n \n \n \n \n\n\n \n Ficsor, A.; and Pintér, B.\n\n\n \n\n\n\n In The 13th Conference of PhD Students in Computer Science, pages 40-44, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"Toolchain pdf\n  \n \n \n \"Toolchain mtmt\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{MTMT:33283785,\n\ttitle = {Toolchain for the Construction of Realistic Simulated Urban Environments},\n\tauthor = {Ficsor, Attila and Pintér, Balázs},\n\tbooktitle = {The 13th Conference of PhD Students in Computer Science},\n\tunique-id = {33283785},\n\tyear = {2022},\n\tpages = {40-44},\n\torcid-numbers = {Ficsor, Attila/0000-0002-0541-4590},\n\n\turl_pdf = {publications/cscs22_2.pdf}, \n\turl_mtmt = {https://m2.mtmt.hu/api/publication/33283785},\n\tabstract = {Testing safety-critical autonomous vehicles (cars, trains, or trams) is an incredibly challenging task: those systems need to interact with an immensely complex and continuously changing environment, making systematic testing unfeasible. Moreover, physical test drives are highly costly, making simulator-based testing a favorable alternative. However, synthetic simulations may fail to provide realistic sensor input, thus hindering the effectiveness of the testing process. This paper proposes a toolchain for the semi-automated construction of realistic road networks using real-world maps. Therefore, better quality simulations can be implemented with higher productivity.},\n}\n\n
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\n Testing safety-critical autonomous vehicles (cars, trains, or trams) is an incredibly challenging task: those systems need to interact with an immensely complex and continuously changing environment, making systematic testing unfeasible. Moreover, physical test drives are highly costly, making simulator-based testing a favorable alternative. However, synthetic simulations may fail to provide realistic sensor input, thus hindering the effectiveness of the testing process. This paper proposes a toolchain for the semi-automated construction of realistic road networks using real-world maps. Therefore, better quality simulations can be implemented with higher productivity.\n
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\n \n\n \n \n \n \n \n \n Semantic Robustness Testing for Vision-Based Machine Learning Components of Autonomous Cyber-Physical Systems.\n \n \n \n \n\n\n \n Ficsor, A.\n\n\n \n\n\n\n In The 13th Conference of PhD Students in Computer Science, pages 35-39, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"Semantic pdf\n  \n \n \n \"Semantic mtmt\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{MTMT:33283734,\n\ttitle = {Semantic Robustness Testing for Vision-Based Machine Learning Components of Autonomous Cyber-Physical Systems},\n\tauthor = {Ficsor, Attila},\n\tbooktitle = {The 13th Conference of PhD Students in Computer Science},\n\tunique-id = {33283734},\n\tyear = {2022},\n\tpages = {35-39},\n\torcid-numbers = {Ficsor, Attila/0000-0002-0541-4590},\n\n\turl_pdf = {publications/cscs22_1.pdf}, \n\turl_mtmt = {https://m2.mtmt.hu/api/publication/33283734},\n\tabstract = {Autonomous cyber-physical systems often utilize vision-based machine learning components. They are frequently part of a safety critical system, requiring special attention during testing. However, testing these systems is an incredibly challenging task, as they need to interact with an immensely complex and continuously changing environment. This makes systematic testing and other safety engineering best practices unfeasible. While some approaches aim to test vision-based machine learning components, these cannot guarantee robustness. In this paper I present a research roadmap of addressing this challenge by (1) proposing a semantic-based robustness testing suite generation approach, (2) determining the minimum level of detail necessary for testing in a simulator, and (3) finding the aspects of the simulation affecting the results. I illustrate my proposed approach on an industrial case study.},\n}\n\n
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\n Autonomous cyber-physical systems often utilize vision-based machine learning components. They are frequently part of a safety critical system, requiring special attention during testing. However, testing these systems is an incredibly challenging task, as they need to interact with an immensely complex and continuously changing environment. This makes systematic testing and other safety engineering best practices unfeasible. While some approaches aim to test vision-based machine learning components, these cannot guarantee robustness. In this paper I present a research roadmap of addressing this challenge by (1) proposing a semantic-based robustness testing suite generation approach, (2) determining the minimum level of detail necessary for testing in a simulator, and (3) finding the aspects of the simulation affecting the results. I illustrate my proposed approach on an industrial case study.\n
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