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12th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation

作者： Bernardeschi, Cinzia Lettieri, Giuseppe Rossi, Federico Univ Pisa Dept Informat Engn Pisa Italy

ISBN: (纸本)9783031751066;9783031751073

In automotive engineering, one of the ideas of vehicle cooperation is to improve convoy movements to increase the safety and efficiency of transportation systems. The complexity and stochastic nature of such cooperative systems pose difficulties for traditional model-checking techniques. statistical model checking (SMC) provides an answer by using statistical inference to evaluate system features in a probabilistic way. In this work, we show how SMC offers a strong framework for assessing vehicle platooning systems in a range of operational scenarios by fusing statistical analysis and formal verification methodologies. Thanks to this approach, we managed to statistically prove a set of safety and functional properties of an autonomous vehicle platoon system.

关键词： Cyber-physical systems statistical model checking Vehicle dynamics Vehicle platooning

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Symbolic state-space exploration meets statistical model checking

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PERFORMANCE EVALUATION 2025年 167卷

作者： Niehage, Mathis Remke, Anne Univ Munster Einsteinstr 62 D-48149 Munster Germany

Efficient reachability analysis, as well as statistical model checking have been proposed for the evaluation of Hybrid Petri nets with general transitions (HPnGs). Both have different (dis-)advantages. The performance of statistical simulation suffers in large models and the number of required simulation runs to achieve a relatively small confidence interval increases considerably. The approach introduced for analytical reachability analysis of HPnGs, however, becomes infeasible for a large number of random variables. To overcome these limitations, this paper applies statistical model checking (SMC) for a stochastic variant of the Signal Temporal Logic (STL) to a pre-computed symbolic state-space representation of HPnGs, i.e., the Parametric Location Tree (PLT), which has previously been used for model checking HPnGs. Furthermore, we define how to reduce the PLT for a given state-based and path-based STL property, by introducing a three-valued interpretation of a given STL property for every location of the PLT. This paper applies learning in the presence of nondeterminism and considers four different scheduler classes. The proposed improvement is especially useful if a large number of training runs is necessary to optimize the probability that a given STL property holds. A case study on a water tank model shows the feasibility of the approach, as well as improved computation times, when applying the above-mentioned reduction for varying time bounds. We validate our results with existing analytical and simulation tools, as applicable for different types of schedulers.

关键词： statistical model checking Symbolic state-space representation Hybrid Petri nets with general transitions Scheduler classes

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Scaling up statistical model checking of cyber-physical systems via algorithm ensemble and parallel simulations over HPC infrastructures

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JOURNAL OF SYSTEMS AND SOFTWARE 2025年 219卷

作者： Picchiami, Leonardo Parmentier, Maxime Legay, Axel Mancini, Toni Tronci, Enrico Sapienza Univ Rome Comp Sci Dept Via Salaria 113 I-00198 Rome Italy Catholic Univ Louvain Comp Sci Dept Pl Sainte Barbe 2 B-1348 Louvain La Neuve Brabant Wallon Belgium

model-based formal verification of industry-relevant Cyber-Physical Systems (CPSs) is often a computationally prohibitive task. In most cases, the complexity of the models precludes any prospect of symbolic analysis, leaving numerical simulation as the only viable option. Unfortunately, exhaustive simulation of a CPS model over the entire set of plausible operational scenarios is rarely possible in practice, and alternative strategies such as statistical model checking (SMC) must be used instead. In this article, we show that the number of model simulations (samples) required by SMC techniques to converge can be significantly reduced by considering multiple (an ensemble of) Adaptive Stopping Algorithms (SAs) at once, and that the simulations themselves (by far the most expensive step of the entire workload) can be efficiently sped up by exploiting massively parallel platforms. With three industry-scale CPS models, we experimentally show that the use of an ensemble of two state-ofthe-art SAs ( AA and EBGStop) may require dozens of millions fewer samples when compared to running a single algorithm, with reductions in sample size of up to 78%. Furthermore, we show that our implementation, by massively parallelizing system model simulations on a HPC infrastructure, yields speed-ups for the completion time of the verification tasks which are practically linear with respect to the number of computational nodes, thus achieving an efficiency of virtually 100%, even on very large platforms. This makes it possible to complete tasks of model-based SMC verification for complex CPSs in a matter of hours or days, whereas a na & iuml;ve sequential execution would require from months to many years.

关键词： Formal verification Cyber-Physical Systems statistical model checking Adaptive Stopping Algorithms Monte Carlo estimation Simulation High-Performance Computing

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Optimal spare management via statistical model checking: a case study in research reactors

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INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER 2025年 1-16页

作者： Soltani, Reza Volk, Matthias Diamonte, Leonardo Lopuhaa-Zwakenberg, Milan Stoelinga, Marielle Univ Twente Enschede Netherlands Eindhoven Univ Technol Eindhoven Netherlands INVAP SE San Carlos de Bariloche Argentina Radboud Univ Nijmegen Nijmegen Netherlands

Systematic spare management is important to optimize the twin goals of high reliability and low costs. However, existing approaches to spare management do not incorporate a detailed analysis of the effect on the absence of spares on the system's reliability. In this work, we combine fault tree analysis with statistical model checking to model spare part management as a stochastic priced timed game automaton (SPTGA). We use Uppaal Stratego to find the number of spares that minimizes the total costs due to downtime and spare purchasing. The resulting SPTGA model can then additionally be analyzed according to a wide range of other metrics, including expected availability. We apply these techniques to the emergency shutdown system of a research nuclear reactor. In this case study, the failure probability is low, so we change the settings of Uppaal Stratego setting to obtain reliable results about rare events. We consider both a single subsystem and the combination of two subsystems. In both situations, our methods find the optimal number of spares, minimizing cost while ensuring an expected availability of 99.96% and 99.93%, respectively.

关键词： Spare management Fault tree statistical model checking Research reactor Uppaal

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White-Box Validation of Collective Adaptive Systems by statistical model checking and Process Mining 12th

White-Box Validation of Collective Adaptive Systems by Stati...

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12th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation

作者： Casalucel, Roberto Tschaikowski, Max Vandin, Andrea St Anna Sch Adv Studies Pisa Pisa Italy Aalborg Univ Aalborg Denmark DTU Tech Univ Denmark Lyngby Denmark

ISBN: (纸本)9783031737084;9783031737091

modeling and analyzing collective adaptive systems with heterogeneous components poses challenges to language designers, software engineers, and computer scientists interested in the formal verification of the modeled systems. This requires the integration of computational, reasoning, and analysis tools, but also of techniques to validate and shed light on the actual behavior described by a model. We build on a methodology built in collaboration with Rocco De Nicola, based on his influential contributions in the modeling and programming of distributed and adaptive systems. Such methodology allows to model and analyze collective adaptive systems equipped with reasoning capabilities. It combines SCEL, a language for programming collective adaptive systems, with Pirlo, a reasoner that can enrich agents with reasoning capabilities, and MultiVeStA, a statistical model checker that can analyze the obtained models by simulating them. Here, we further enrich this methodology with techniques from process-oriented data science, and in particular process mining, to ease the validation and debugging of such models. This follows recent proposals from the literature that validate models by explaining graphically the behaviors observed by a statistical model checker. We demonstrate our approach by considering a simple collision-avoidance robotic scenario where a group of robots moves in an arena while aiming at minimizing the number of collisions.

关键词： SCEL statistical model checking Process mining

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statistical model checking-Based Evaluation and Optimization for Cloud Workflow Resource Allocation

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2020年第2期8卷 443-458页

作者： Chen, Mingsong Huang, Saijie Fu, Xin Liu, Xiao He, Jifeng East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai 200062 Peoples R China Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA Deakin Univ Sch Informat Technol Melbourne Vic 3122 Australia

Due to the existence of resource variations, it is very challenging for Cloud workflow resource allocation strategies to guarantee a reliable Quality of Service (QoS). Although dozens of resource allocation heuristics have been developed to improve the QoS of Cloud workflow, it is hard to predict their performance under variations because of the lack of accurate modeling and evaluation methods. So far, there is no comprehensive approach that can quantitatively reason the capability of resource allocation strategies or enable the tuning of parameters to optimize resource allocation solutions under variations. To address the above problems, this paper proposes a novel framework that can evaluate and optimize resource allocation strategies effectively and quantitatively. By using the statistical model checker UPPAAL-SMC and supervised learning approaches, our framework can: i) conduct complex QoS queries on resource allocation instances considering resource variations;ii) make quantitative and qualitative comparisons among resource allocation strategies;iii) enable the tuning of parameters to improve the overall QoS;and iv) support the quick optimization of overall workflow QoS under customer requirements and resource variations. The experimental results demonstrate that our automated framework can support both the Service Level Agreement (SLA) negotiation and workflow resource allocation optimization efficiently.

关键词： Resource management Cloud computing Quality of service Optimization Software as a service Stochastic processes Unified modeling language Cloud computing statistical model checking optimization resource allocation strategy service level agreement

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statistical model checking for variability-intensive systems: applications to bug detection and minimization

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FORMAL ASPECTS OF COMPUTING 2021年第6期33卷 1147-1172页

作者： Cordy, Maxime Lazreg, Sami Papadakis, Mike Legay, Axel Univ Luxembourg SnT Esch Sur Alzette Luxembourg Catholic Univ Louvain Ottignies Belgium

We propose a new statistical model checking (SMC) method to identify bugs in variability-intensive systems (VIS). The state-space of such systems is exponential in the number of variants, which makes the verification problem harder than for classical systems. To reduce verification time, we propose to combine SMC with featured transition systems (FTS)-a model that represents jointly the state spaces of all variants. Our new methods allow the sampling of executions from one or more (potentially all) variants. We investigate their utility in two complementary use cases. The first case considers the problem of finding all variants that violate a given property expressed in Linear-Time Logic (LTL) within a given simulation budget. To achieve this, we perform random walks in the featured transition system seeking accepting lassos. We show that our method allows us to find bugs much faster (up to 16 times according to our experiments) than exhaustive methods. As any simulation-based approach, however, the risk of Type-1 error exists. We provide a lower bound and an upper bound for the number of simulations to perform to achieve the desired level of confidence. Our empirical study involving 59 properties over three case studies reveals that our method manages to discover all variants violating 41 of the properties. This indicates that SMC can act as a coarse-grained analysis method to quickly identify the set of buggy variants. The second case complements the first one. In case the coarse-grained analysis reveals that no variant can guarantee to satisfy an intended property in all their executions, one should identify the variant that minimizes the probability of violating this property. Thus, we propose a fine-grained SMC method that quickly identifies promising variants and accurately estimates their violation probability. We evaluate different selection strategies and reveal that a genetic algorithm combined with elitist selection yields the best results.

关键词： statistical model checking Variability Verification Simulation Sampling

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statistical model checking for biological systems

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INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER 2015年第3期17卷 351-367页

作者： David, Alexandre Larsen, Kim G. Legay, Axel Mikucionis, Marius Poulsen, Danny Bogsted Sedwards, Sean Aalborg Univ Dept Comp Sci Aalborg Denmark INRIA Rennes Bretagne Atlantique Rennes France

statistical model checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic timed automata and most recently hybrid systems using the tool UPPAAL SMC. In this paper we enable the application of SMC to complex biological systems, by combining UPPAAL SMC with ANIMO, a plugin of the tool Cytoscape used by biologists, as well as with SimBiology (R), a plugin of MATLAB to simulate reactions. ANIMO and SimBiology (R) are two domain specific tools that have their own user interfaces and formalisms specifically tailored towards the biology domain. However-though providing means for simulation-both tools lack the powerful analytic capabilities offered by SMC, which in previous work have proved very useful for identifying interesting properties of biological systems. Our aim is to offer the best of the two worlds: optimal domain specific interfaces and formalisms suited to biology combined with powerful SMC analysis techniques for stochastic and hybrid systems. This goal is obtained by developing translators from the XGMML and SBML formats used by Cytoscape and SimBiology (R) to stochastic and hybrid automata, allowing UPPAAL SMC to be used as an efficient backend analysis tool, that we demonstrate can handle real-world biological systems by pitting it against the Biomodels database. We present detailed analysis on two particular case-studies involving the ANIMO and SimBiology (R) tools.

关键词： statistical model checking UPPAAL Systems biology

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statistical model checking QoS properties of systems with SBIP

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INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER 2015年第2期17卷 171-185页

作者： Nouri, Ayoub Bensalem, Saddek Bozga, Marius Delahaye, Benoit Jegourel, Cyrille Legay, Axel UJF Grenoble 1 CNRS VERIMAG UMR 5104 F-38041 Grenoble France Univ Nantes LINA Nantes France INRIA IRISA Rennes France

Behavior-interaction-priority (BIP) is a component-based framework supporting rigorous design of embedded systems. BIP supports incremental design of large systems from atomic components that communicate via connectors and whose interactions can be described with a powerful algebra. This paper presents SBIP, an extension of BIP for stochastic systems. SBIP offers the possibility to add stochastic information to atomic component's behaviors, and hence to the entire system. Atomic component's semantics in SBIP is described by Markov Chains. We show that the semantics of the entire system is described by a Markov chain, showing that the non-determinism arising from system interactions is automatically eliminated by BIP. This allows us to verify systems described in SBIP with statistical model checking. This paper introduces SBIP and illustrates its usability on several industrial case studies.

关键词： Component-based design Stochastic systems statistical model checking Experiments

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statistical model checking for Stochastic and Hybrid Autonomous Driving Based on Spatio-Clock Constraints

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2022年第4期32卷 553-582页

作者： Wang, Jinyong Huang, Zhiqiu Zhu, Yi Shen, Guohua Xuzhou Univ Technol Sch Informat Engn Xuzhou 221018 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China

Autonomous driving vehicles are a kind of typical cyber-physical systems integrating complex interactions between hardware and software components such as collaborative computation, distributed communication, and spatio-clock synchronous control with surrounding traffic environment. They can percept the environment, communicate with surroundings, and react fast enough to control independently. The purpose of autonomous driving emergence is to improve driving safety, reduce environmental pollution, and ease the traffic congestion. However, new features with surrounding open and dynamic environment make systems design and verification becoming more and more complex than ever, such as stochastic communication delay, hardware spontaneous failure distribution, and natively hybrid behaviors described by ordinary differential equations. Spatial and time collision avoidance remains crucial obstacles on the path to becoming ubiquitous and dependable. In this paper, we adopt statistical model checking (SMC) to enlighten possible hazards affected by stochastic and hybrid features in the design phase of autonomous driving systems. In order to provide safety and accountability, we first propose a dedicated multi-lane spatio-clock stochastic specification language (MLSCL) to describe safety invariants and guards in domain-specific autonomous driving systems. Then, we present the semantic mapping rules between MLSCL and UPPAAL SMC models, and design the spatio-clock stochastic and hybrid automata based on MLSCL in order to model inherently stochastic and hybrid behaviors. Finally, we present an illustrative lane-change case study to verify spatio-clock stochastic and hybrid-related properties adopting SMC, and demonstrate the effectiveness of our proposed approach.

关键词： Spatio-clock synchronous semantic mapping rules stochastic and hybrid automata statistical model checking autonomous driving safety

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