Component-Based Synthesis of Embedded Systems Using Satisfiability Modulo Theories

Abstract

Constraint programming solvers, such as Satisfiability Modulo Theory (SMT) solvers, are capable tools in finding preferable configurations for embedded systems from large design spaces. However, constructing SMT constraint programs is not trivial, in particular for complex systems that exhibit multiple viewpoints and models. In thisarticle we propose CoDeL: a component-based description language that allows system designers to express components as reusable building blocks of the system with their parameterizable properties, models, and interconnectivity. Systems are synthesized by allocating, connecting, and parameterizing the components to satisfy the requirements of an application. We present an algorithm that transforms component-based design spaces, expressible in CoDeL, to an SMT program, which, solved by state-of-the-art SMT solvers, determines the satisfiability of the synthesis problem, and delivers a correct-by-construction system configuration. Evaluation results for use cases in the domain of scheduling and mapping of distributed real-time processes confirm, first, the performance gain of SMT compared to traditional design space exploration approaches, second, the usability gains by expressing design problems in CoDeL, and third, the capability of the CoDeL/SMT approach to support the design of embedded systems.

Automating integration under emergent constraints for embedded systems

“…However, as soon as software layers differ, a particular component might need to be integrated differently when deployed on another hardware component. There are approaches that explicitly address the software composition problem [21] using constraint-based methods such as SMT [21] or Mixed Integer Linear Programming (MILP) [14], however, they do not consider composite components. As soon as the number of required software components, tasks, etc.…”

Automating System Configuration

“…Early approaches were based on constraint satisfaction and constraint logic programming [17], [24], [25]. More recent approaches utilize SAT and SMT solvers [16], [18], [26], and counterexample-guided inductive synthesis and relational model finding [20], [27] for dynamic configuration. However, the way these approaches reduce configuration problems to constraint satisfaction problems is significantly different from our approach using input/output examples and unrolling.…”

Demonstrating Controlled Change for Autonomous Space Vehicles

“…The main limitation of these approaches is that the software composition is already known and fixed. There are approaches that explicitly address the software composition problem using constraint-based methods such as SMT [9] or Mixed Integer Linear Programming (MILP) [10], however, they do not consider cases in which the number of component varies (cf. Fig.…”