Towards Pareto-optimal parameter synthesis for monotonie cost functions

Designers are often required to explore alternative solutions, trading off along different dimensions (e.g., power consumption, weight, cost, reliability, response time). Such exploration can be encoded as a problem of parameter synthesis, i.e., finding a parameter valuation (representing a design solution) such that the corresponding system satisfies a desired property. In this paper, we tackle the problem of parameter synthesis with multi-dimensional cost functions by finding solutions that are in the Pareto front: in the space of best trade-offs possible. We propose several algorithms, based on IC3, that interleave in various ways the search for parameter valuations that satisfy the property, and the optimization with respect to costs. The most effective one relies on the reuse of inductive invariants and on the extraction of unsatisfiable cores to accelerate convergence. Our experimental evaluation shows the feasibility of the approach on practical benchmarks from diagnosability synthesis and productline engineering, and demonstrates the importance of a tight integration between model checking and cost optimization

Towards Pareto-Optimal Parameter Synthesis for Monotonic Cost Functions

Designers are often required to explore alternative solutions, trading off along different dimensions (e.g., power consumption, weight, cost, reliability, response time). Such exploration can be encoded as a problem of parameter synthesis, i.e., finding a parameter valuation (representing a design solution) such that the corresponding system satisfies a desired property. In this paper, we tackle the problem of parameter synthesis with multi-dimensional cost functions by finding solutions that are in the Pareto front: in the space of best trade-offs possible. We propose several algorithms, based on IC3, that interleave in various ways the search for parameter valuations that satisfy the property, and the optimization with respect to costs. The most effective one relies on the reuse of inductive invariants and on the extraction of unsatisfiable cores to accelerate convergence. Our experimental evaluation shows the feasibility of the approach on practical benchmarks from diagnosability synthesis and productline engineering, and demonstrates the importance of a tight integration between model checking and cost optimization

FAME: A Model-Based Environment for FDIR Design in Aerospace

The FAME environment is a model-based toolset that implements an integrated process for FDIR (Fault Detection, Isolation and Recovery) design, addressing the shortcomings of existing practices for FDIR development in aerospace. It is built on top of COMPASS, a framework for model-based design and verification, that provides several verification capabilities, including simulation, property verification, RAMS analysis (FTA, FMEA), diagnosability and FDIR analysis. The FAME environment supports FDIR design by providing functionality to define mission and FDIR requirements, fault propagation modeling using TFPGs (Timed Fault Propagation Graphs), and automated synthesis of FDIR models from TFPGs and FDIR requirements. The FAME environment has been developed within an ESA-funded study, and has been thoroughly evaluated by the industrial partners on a case study derived from the ExoMars project

Automated generation of FDIR for the compass integrated toolset (AUTOGEF)

The ESA AUTOGEF (Dependability Design Approach for Critical Flight Software) study is a direct follow-on of the ESA TRP COMPASS (Correctness, Modelling and Performance of Aerospace Systems). The aim of COMPASS project was to develop a modelbased approach to system-software co-engineering, tailored to the specifics of critical on-board spacecraft systems. COMPASS included the development of a platform based on formal methods, which offers a wide range of techniques for system verification and validation. AUTOGEF aims to demonstrate that synthesis approaches can allow for effective automated FDIR development in accordance with the dependability requirements, through the implementation of an add-on to the COMPASS tool