Détails sur le séminaire

Abstract: In this thesis, we address the problem of modeling and verification of complex systems exhibiting both probabilistic and timed behaviors. Designing such systems has become increasingly complex due to the heterogeneity of the involved components, the uncertainty resulting from open environment and the real-time constraints inherent to their application domains. Handling both software and (abstraction of) hardware in a unified view while also including performance information (e.g. computation and communication times, energy consumption, etc.) becomes a must. Building and analyzing performance models is of paramount importance in order to give guarantees on the functional and extra-functional system requirements and to make well-founded design decisions based on quantitative measures at early design stages. This thesis brings several new contributions.
First, we introduce a new modeling formalism called Stochastic Real-Time BIP (SRT-BIP) for the modeling, the simulation and the code generation of component-based systems. This formalism inherits from the BIP framework its component-based and real-time modeling capabilities and, extends it by providing comprehensive primitives to express complex stochastic behaviors.
Second, we investigate machine learning techniques to ease the construction of performance models. We propose to enhance and adapt a state-of-the-art learning procedure to infer stochastic real-time models from concrete system execution and to represent them in the SRT-BIP formalism.
Third, given performance models in SRT-BIP, we explore the use of statistical Model Checking (SMC) for the anaysis of system’s functional and performance requirements. To do so, we provide a full framework, called SBIP, as a support tool for the modeling, simulation and analysis of SRT-BIP systems. SBIP is an Integrated Development Environment (IDE) that implements SMC algorithms for quantitative, qualitative and rare events analyses together with an automated exploring procedure for parameterized requirements. We validate our proposals on real-life case studies ranging from communication protocols and concurrent systems to embedded systems.
Finally, we further investigate the interest of SMC when included in elaborated system analysis workflows. We illustrate this by proposing two risk assessment approaches. In the first approach, we introduce a spiral methodology to build resilient systems with FDIR components that we validate on the safety assessment of a planetary rover locomotion system. The second approach is concerned with the security assessment of organization’s defenses following an offensive security approach. The goal is to synthesize impactful defense configurations against optimized attack strategies (that minimize attack cost and maximize success probability). These attack strategies are obtained by combining model learning with meta-heuristics, and where SMC is used to score and prioritize potential candidate strategies.
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