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Ed Kuijpers, Luigi Carotenuto, Jean- Cristophe Malapert, Daniela Markov-Vetter, Igor Melatti, Andrea Orlandini, and Ranni Pinchuk. "Collaboration on ISS Experiment Data and Knowledge Representation." In Proc. of IAC 2012. Vol. D.5.11., 2012.
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Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "Synthesizing Control Software from Boolean Relations." International Journal on Advances in Software vol. 5, nr 3&4 (2012): 212–223. IARIA. ISSN: 1942-2628.
Abstract: Many software as well digital hardware automatic
synthesis methods define the set of
implementations meeting the given system
specifications with a boolean relation K. In
such a context a fundamental step in the software
(hardware) synthesis process is finding effective
solutions to the functional equation defined by
K. This entails finding a (set of) boolean
function(s) F (typically represented using
OBDDs, Ordered Binary Decision Diagrams)
such that: 1) for all x for which K is
satisfiable, K(x, F(x)) = 1 holds; 2) the
implementation of F is efficient with respect
to given implementation parameters such as code
size or execution time. While this problem has
been widely studied in digital hardware synthesis,
little has been done in a software synthesis
context. Unfortunately, the approaches developed
for hardware synthesis cannot be directly used in
a software context. This motivates investigation
of effective methods to solve the above problem
when F has to be implemented with software. In
this paper, we present an algorithm that, from an
OBDD representation for K, generates a C code
implementation for F that has the same size as
the OBDD for F and a worst case execution time
linear in nr, being n = |x| the number of
input arguments for functions in F and r the
number of functions in F. Moreover, a formal
proof of the proposed algorithm correctness is
also shown. Finally, we present experimental
results showing effectiveness of the proposed
algorithm.
Keywords: Control Software Synthesis; Embedded Systems; Model Checking
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Federico Mari, Igor Melatti, Enrico Tronci, and Alberto Finzi. "A multi-hop advertising discovery and delivering protocol for multi administrative domain MANET." Mobile Information Systems 3, no. 9 (2013): 261–280. IOS Press. ISSN: 1574-017x (Print) 1875-905X (Online). DOI: 10.3233/MIS-130162.
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Toni Mancini, Federico Mari, Annalisa Massini, Igor Melatti, Fabio Merli, and Enrico Tronci. "System Level Formal Verification via Model Checking Driven Simulation." In Proceedings of the 25th International Conference on Computer Aided Verification. July 13-19, 2013, Saint Petersburg, Russia, 296–312. Lecture Notes in Computer Science 8044. Springer - Verlag, 2013. ISSN: 0302-9743. DOI: 10.1007/978-3-642-39799-8_21.
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Toni Mancini, Federico Mari, Annalisa Massini, Igor Melatti, and Enrico Tronci. "System Level Formal Verification via Distributed Multi-Core Hardware in the Loop Simulation." In Proc. of the 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing. IEEE Computer Society, 2014. DOI: 10.1109/PDP.2014.32.
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Verzino Giovanni, Federico Cavaliere, Federico Mari, Igor Melatti, Giovanni Minei, Ivano Salvo, Yuri Yushtein, and Enrico Tronci. "Model checking driven simulation of sat procedures." In Proceedings of 12th International Conference on Space Operations (SpaceOps 2012)., 2012. DOI: 10.2514/6.2012-1275611.
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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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E. Tronci, T. Mancini, F. Mari, I. Melatti, R. H. Jacobsen, E. Ebeid, S. A. Mikkelsen, M. Prodanovic, J. K. Gruber, and B. Hayes. "SmartHG: Energy Demand Aware Open Services for Smart Grid Intelligent Automation." In Proceedings of the Work in Progress Session of SEAA/DSD 2014., 2014.
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E. Tronci, T. Mancini, I. Salvo, F. Mari, I. Melatti, A. Massini, S. Sinisi, F. Davì, T. Dierkes, R. Ehrig et al. "Patient-Specific Models from Inter-Patient Biological Models and Clinical Records." In Formal Methods in Computer-Aided Design (FMCAD)., 2014. DOI: 10.1109/FMCAD.2014.6987615.
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E. Tronci, T. Mancini, F. Mari, I. Melatti, I. Salvo, M. Prodanovic, J. K. Gruber, B. Hayes, and L. Elmegaard. "Demand-Aware Price Policy Synthesis and Verification Services for Smart Grids." In Proceedings of Smart Grid Communications (SmartGridComm), 2014 IEEE International Conference On., 2014. DOI: 10.1109/SmartGridComm.2014.7007745.
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