@article{MTMT:34375495,
	title = {Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search},
	url = {https://m2.mtmt.hu/api/publication/34375495},
	author = {Babikian, Aren A. and Semeráth, Oszkár and Varró, Dániel},
	doi = {10.1109/TSE.2023.3331254},
	journal-iso = {IEEE T SOFTWARE ENG},
	journal = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING},
	volume = {50},
	unique-id = {34375495},
	issn = {0098-5589},
	abstract = {Existing safety assurance approaches for autonomous vehicles (AVs) perform system-level safety evaluation by placing the AV-under-test in challenging traffic scenarios captured by abstract scenario specifications and investigated in realistic traffic simulators. As a first step towards scenario-based testing of AVs, the initial scene of a traffic scenario must be concretized. In this context, the scene concretization challenge takes as input a high-level specification of abstract traffic scenes and aims to map them to concrete scenes where exact numeric initial values are defined for each attribute of a vehicle (e.g. position or velocity). In this paper, we propose a traffic scene concretization approach that places vehicles on realistic road maps such that they satisfy an extensible set of abstract constraints defined by an expressive scene specification language which also supports static detection of inconsistencies. Then, abstract constraints are mapped to corresponding numeric constraints, which are solved by metaheuristic search with customizable objective functions and constraint aggregation strategies. We conduct a series of experiments over three realistic road maps to compare eight configurations of our approach with three variations of the state-of-the-art Scenic tool, and to evaluate its scalability.},
	keywords = {Multiobjective optimization; evolutionary algorithm; Computer Science, Software Engineering; Scenario Description Language; Assurance for autonomous vehicles; traffic scene concretization},
	year = {2024},
	eissn = {1939-3520},
	pages = {48-68},
	orcid-numbers = {Varró, Dániel/0000-0002-8790-252X}
}

@inproceedings{MTMT:33648748,
	title = {Consistent Scene Graph Generation by Constraint Optimization},
	url = {https://m2.mtmt.hu/api/publication/33648748},
	author = {Chen, Boqi and Marussy, Kristóf and Pilarski, Sebastian and Semeráth, Oszkár and Varró, Dániel},
	booktitle = {ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering},
	doi = {10.1145/3551349.3560433},
	unique-id = {33648748},
	year = {2022},
	pages = {1-13},
	orcid-numbers = {Marussy, Kristóf/0000-0002-9135-8256; Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:32589473,
	title = {Delayed Reward Bernoulli Bandits: Optimal Policy and Predictive Meta-Algorithm PARDI},
	url = {https://m2.mtmt.hu/api/publication/32589473},
	author = {Pilarski, Sebastian and Pilarski, Slawomir and Varró, Dániel},
	doi = {10.1109/TAI.2021.3117743},
	journal-iso = {IEEE Trans. Artif. Intell.},
	journal = {IEEE Transactions on Artificial Intelligence},
	volume = {3},
	unique-id = {32589473},
	year = {2022},
	eissn = {2691-4581},
	pages = {152-163},
	orcid-numbers = {Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:32589305,
	title = {An Empirical Study of Type-Related Defects in Python Projects},
	url = {https://m2.mtmt.hu/api/publication/32589305},
	author = {Khan, Faizan and Chen, Boqi and Varró, Dániel and Mcintosh, Shane},
	doi = {10.1109/TSE.2021.3082068},
	journal-iso = {IEEE T SOFTWARE ENG},
	journal = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING},
	volume = {48},
	unique-id = {32589305},
	issn = {0098-5589},
	abstract = {In recent years, Python has experienced explosive growth in adoption, particularly among open source projects. While Python's dynamically-typed nature provides developers with powerful programming abstractions, that same dynamic type system allows for type-related defects to accumulate in code bases. To aid in the early detection of type-related defects, type annotations were introduced into the Python ecosystem (i.e., PEP-484) and static type checkers like mypy have appeared on the market. While applying a type checker like mypy can in theory help to catch type-related defects before they impact users, little is known about the real impact of adopting a type checker to reveal defects in Python projects. In this paper, we study the extent to which Python projects benefit from such type checking features. For this purpose, we mine the issue tracking and version control repositories of 210 Python projects on GitHub. Inspired by the work of Gao et al. on type-related defects in JavaScript, we add type annotations to test whether detects an error that would have helped developers to avoid real defects. We observe that 15% of the defects could have been prevented by mypy. Moreover, we find that there is no significant difference between the experience level of developers committing type-related defects and the experience of developers committing defects that are not type-related. In addition, a manual analysis of the anti-patterns that most commonly lead to type-checking faults reveals that the redefinition of Python references, dynamic attribute initialization and incorrectly handled Null objects are the most common causes of type-related faults. Since our study is conducted on fixed public defects that have gone through code reviews and multiple test cycles, these results represent a lower bound on the benefits of adopting a type checker. Therefore, we recommend incorporating a static type checker like mypy into the development workflow, as not only will it prevent type-related defects but also mitigate certain anti-patterns during development. IEEE},
	year = {2022},
	eissn = {1939-3520},
	pages = {3145-3158},
	orcid-numbers = {Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:32394785,
	title = {Automated generation of consistent models using qualitative abstractions and exploration strategies},
	url = {https://m2.mtmt.hu/api/publication/32394785},
	author = {Babikian, Aren A. and Semeráth, Oszkár and Li, Anqi and Marussy, Kristóf and Varró, Dániel},
	doi = {10.1007/s10270-021-00918-6},
	journal-iso = {SOFTW SYST MODEL},
	journal = {SOFTWARE AND SYSTEMS MODELING},
	volume = {21},
	unique-id = {32394785},
	issn = {1619-1366},
	abstract = {Automatically synthesizing consistent models is a key prerequisite for many testing scenarios in autonomous driving to ensure a designated coverage of critical corner cases. An inconsistent model is irrelevant as a test case (e.g., false positive); thus, each synthetic model needs to simultaneously satisfy various structural and attribute constraints, which includes complex geometric constraints for traffic scenarios. While different logic solvers or dedicated graph solvers have recently been developed, they fail to handle either structural or attribute constraints in a scalable way. In the current paper, we combine a structural graph solver that uses partial models with an SMT-solver and a quadratic solver to automatically derive models which simultaneously fulfill structural and numeric constraints, while key theoretical properties of model generation like completeness or diversity are still ensured. This necessitates a sophisticated bidirectional interaction between different solvers which carry out consistency checks, decision, unit propagation, concretization steps. Additionally, we introduce custom exploration strategies to speed up model generation. We evaluate the scalability and diversity of our approach, as well as the influence of customizations, in the context of four complex case studies.},
	keywords = {Model generation; Exploration strategy; Partial model; Graph solver; SMT-solver; Numeric solver; Test scenario synthesis},
	year = {2022},
	eissn = {1619-1374},
	pages = {1763-1787},
	orcid-numbers = {Marussy, Kristóf/0000-0002-9135-8256; Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:31360895,
	title = {Automated Generation of Consistent Graph Models with Multiplicity Reasoning},
	url = {https://m2.mtmt.hu/api/publication/31360895},
	author = {Marussy, Kristóf and Semeráth, Oszkár and Varró, Dániel},
	doi = {10.1109/TSE.2020.3025732},
	journal-iso = {IEEE T SOFTWARE ENG},
	journal = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING},
	volume = {48},
	unique-id = {31360895},
	issn = {0098-5589},
	abstract = {Advanced tools used in model-based systems engineering (MBSE) frequently represent their models as graphs. In order to test those tools, the automated generation of well-formed (or intentionally malformed) graph models is necessitated which is often carried out by solver-based model generation techniques. In many model generation scenarios, one needs more refined control over the generated unit tests to focus on the more relevant models. Type scopes allow to precisely define the required number of newly generated elements, thus one can avoid the generation of unrealistic and highly symmetric models having only a single type of elements. In this paper, we propose a 3-valued scoped partial modeling formalism, which innovatively extends partial graph models with predicate abstraction and counter abstraction. As a result, well-formedness constraints and multiplicity requirements can be evaluated in an approximated way on incomplete (unfinished) models by using advanced graph query engines with numerical solvers (e.g., IP or LP solvers). Based on the refinement of 3-valued scoped partial models, we propose an efficient model generation algorithm that generates models that are both well-formed and satisfy the scope requirements. We show that the proposed approach scales significantly better than existing SAT-solver techniques or the original graph solver without multiplicity reasoning. We illustrate our approach in a complex design-space exploration case study of collaborating satellites introduced by researchers at NASA JPL.},
	year = {2022},
	eissn = {1939-3520},
	pages = {1610-1629},
	orcid-numbers = {Marussy, Kristóf/0000-0002-9135-8256; Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:32589303,
	title = {Optimal Policy for Bernoulli Bandits: Computation and Algorithm Gauge},
	url = {https://m2.mtmt.hu/api/publication/32589303},
	author = {Pilarski, Sebastian and Pilarski, Slawomir and Varró, Dániel},
	doi = {10.1109/TAI.2021.3074122},
	journal-iso = {IEEE Trans. Artif. Intell.},
	journal = {IEEE Transactions on Artificial Intelligence},
	volume = {2},
	unique-id = {32589303},
	year = {2021},
	eissn = {2691-4581},
	pages = {2-17},
	orcid-numbers = {Pilarski, Sebastian/0000-0002-4942-0757; Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:32589291,
	title = {Worst-case Execution Time Calculation for Query-based Monitors by Witness Generation},
	url = {https://m2.mtmt.hu/api/publication/32589291},
	author = {Búr, Márton and Marussy, Kristóf and Meyer, Brett H. and Varró, Dániel},
	doi = {10.1145/3471904},
	journal-iso = {ACM T EMBED COMPUT S},
	journal = {ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS},
	volume = {20},
	unique-id = {32589291},
	issn = {1539-9087},
	year = {2021},
	eissn = {1558-3465},
	pages = {1-36},
	orcid-numbers = {Búr, Márton/0000-0003-2702-6174; Marussy, Kristóf/0000-0002-9135-8256; Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:32399177,
	title = {Predictions-on-chip: model-based training and automated deployment of machine learning models at runtime For multi-disciplinary design and operation of gas turbines},
	url = {https://m2.mtmt.hu/api/publication/32399177},
	author = {Pilarski, Sebastian and Staniszewski, Martin and Bryan, Matthew and Villeneuve, Frederic and Varró, Dániel},
	doi = {10.1007/s10270-020-00856-9},
	journal-iso = {SOFTW SYST MODEL},
	journal = {SOFTWARE AND SYSTEMS MODELING},
	volume = {20},
	unique-id = {32399177},
	issn = {1619-1366},
	abstract = {The design of gas turbines is a challenging area of cyber-physical systems where complex model-based simulations across multiple disciplines (e.g., performance, aerothermal) drive the design process. As a result, a continuously increasing amount of data is derived during system design. Finding new insights in such data by exploiting various machine learning (ML) techniques is a promising industrial trend since better predictions based on real data result in substantial product quality improvements and cost reduction. This paper presents a method that generates data from multi-paradigm simulation tools, develops and trains ML models for prediction, and deploys such prediction models into an active control system operating at runtime with limited computational power. We explore the replacement of existing traditional prediction modules with ML counterparts with different architectures. We validate the effectiveness of various ML models in the context of three (real) gas turbine bearings using over 150,000 data points for training, validation, and testing. We introduce code generation techniques for automated deployment of neural network models to industrial off-the-shelf programmable logic controllers.},
	keywords = {NEURAL NETWORKS; machine learning; Code generation; gas turbine engines; Prediction-at-runtime; Automated deployment},
	year = {2021},
	eissn = {1619-1374},
	pages = {685-709},
	orcid-numbers = {Pilarski, Sebastian/0000-0002-4942-0757; Varró, Dániel/0000-0002-8790-252X}
}

@article{MTMT:32028274,
	title = {Automated Generation of Consistent, Diverse and Structurally Realistic Graph Models},
	url = {https://m2.mtmt.hu/api/publication/32028274},
	author = {Semeráth, Oszkár and Aren, A. Babikian and Boqi, Chen and Chuning, Li and Marussy, Kristóf and Szárnyas, Gábor and Varró, Dániel},
	doi = {10.1007/s10270-021-00884-z},
	journal-iso = {SOFTW SYST MODEL},
	journal = {SOFTWARE AND SYSTEMS MODELING},
	volume = {20},
	unique-id = {32028274},
	issn = {1619-1366},
	abstract = {In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.},
	year = {2021},
	eissn = {1619-1374},
	pages = {1713-1734},
	orcid-numbers = {Marussy, Kristóf/0000-0002-9135-8256; Varró, Dániel/0000-0002-8790-252X}
}