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T. Mancini, F. Mari, A. Massini, I. Melatti, I. Salvo, and E. Tronci. "On minimising the maximum expected verification time." Information Processing Letters (2017). DOI: 10.1016/j.ipl.2017.02.001.
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Ruggero Lanotte, Andrea Maggiolo-Schettini, Simone Tini, Angelo Troina, and Enrico Tronci. "Automatic Covert Channel Analysis of a Multilevel Secure Component." In Information and Communications Security, 6th International Conference, ICICS 2004, Malaga, Spain, October 27-29, 2004, Proceedings, edited by J. Lopez, S. Qing and E. Okamoto, 249–261. Lecture Notes in Computer Science 3269. Springer, 2004. DOI: 10.1007/b101042.
Abstract: The NRL Pump protocol defines a multilevel secure component whose goal is to minimize leaks of information from high level systems to lower level systems, without degrading average time performances. We define a probabilistic model for the NRL Pump and show how a probabilistic model checker (FHP-mur$\varphi$) can be used to estimate the capacity of a probabilistic covert channel in the NRL Pump. We are able to compute the probability of a security violation as a function of time for various configurations of the system parameters (e.g. buffer sizes, moving average size, etc). Because of the model complexity, our results cannot be obtained using an analytical approach and, because of the low probabilities involved, it can be hard to obtain them using a simulator.
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A. Pappagallo, A. Massini, and E. Tronci. "Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review." Information 11, no. 558 (2020). DOI: 10.3390/info11120588.
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Giuseppe Della Penna, Daniele Magazzeni, Alberto Tofani, Benedetto Intrigila, Igor Melatti, and Enrico Tronci. "Automated Generation Of Optimal Controllers Through Model Checking Techniques." In Informatics in Control Automation and Robotics. Selected Papers from ICINCO 2006, 107–119. Springer, 2008. DOI: 10.1007/978-3-540-79142-3_10.
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Corrado Böhm, and Enrico Tronci. "About Systems of Equations, X-Separability, and Left-Invertibility in the lambda-Calculus." Inf. Comput. 90, no. 1 (1991): 1–32. DOI: 10.1016/0890-5401(91)90057-9.
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Silvia Mazzini, Stefano Puri, Federico Mari, Igor Melatti, and Enrico Tronci. "Formal Verification at System Level." In In: DAta Systems In Aerospace (DASIA), Org. EuroSpace, Canadian Space Agency, CNES, ESA, EUMETSAT. Instanbul, Turkey, EuroSpace., 2009.
Abstract: System Level Analysis calls for a language comprehensible to experts with different background and yet precise enough to support meaningful analyses. SysML is emerging as an effective balance between such conflicting goals. In this paper we outline some the results obtained as for SysML based system level functional formal verification by an ESA/ESTEC study, with a collaboration among INTECS and La Sapienza University of Roma. The study focuses on SysML based system level functional requirements techniques.
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Toni Mancini, Federico Mari, Annalisa Massini, Igor Melatti, and Enrico Tronci. "Anytime System Level Verification via Random Exhaustive Hardware In The Loop Simulation." In In Proceedings of 17th EuroMicro Conference on Digital System Design (DSD 2014)., 2014. DOI: 10.1109/DSD.2014.91.
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Amedeo Cesta, Alberto Finzi, Simone Fratini, Andrea Orlandini, and Enrico Tronci. "Merging Planning, Scheduling & Verification – A Preliminary Analysis." In In Proc. of 10th ESA Workshop on Advanced Space Technologies for Robotics and Automation (ASTRA)., 2008.
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Amedeo Cesta, Alberto Finzi, Simone Fratini, Andrea Orlandini, and Enrico Tronci. "Validation and Verification Issues in a Timeline-based Planning System." In In E-Proc. of ICAPS Workshop on Knowledge Engineering for Planning and Scheduling., 2008.
Abstract: One of the key points to take into account to foster effective introduction of AI planning and scheduling systems in real world is to develop end user trust in the related technologies. Automated planning and scheduling systems often brings solutions to the users which are neither “obviousÃ¢â‚¬Âť nor immediately acceptable for them. This is due to the ability of these tools to take into account quite an amount of temporal and causal constraints and to employ resolution processes often designed to optimize the solution with respect to non trivial evaluation functions. To increase technology trust, the study of tools for verifying and validating plans and schedules produced by AI systems might be instrumental. In general, validation and verification techniques represent a needed complementary technology in developing domain independent architectures for automated problem solving. This paper presents a preliminary report of the issues concerned with the use of two software tools for formal verification of finite state systems to the validation of the solutions produced by MrSPOCK, a recent effort for building a timeline based planning tool in an ESA project.
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T. Mancini, I. Melatti, and E. Tronci. "Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification." IEEE Transactions on Software Engineering (2021): 1. ISSN: 1939-3520. Notes: To appear. DOI: 10.1109/TSE.2021.3109842.
Abstract: Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding </i>vacuity</i> in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking.
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