Scalable and Optimized Hybrid Verification of Embedded Software

Abstract

The verification of embedded software has become an important subject over the last years. However, neither standalone verification approaches, like simulation-based/formal verification, nor state-of-the-art semiformal verification approaches are able to verify large and complex embedded software with or without hardware dependencies. This work presents a scalable hybrid verification approach for the verification of embedded software using a semiformal algorithm optimized with static parameter assignment (SPA). These algorithms and methodologies like SPA and counterexample guided simulation are used to combine simulation-based and formal verification in a new way. SPA offers a method to interact between dynamic and static verification approaches based on an automated ranking determination of possible function parameters according to the impact on the model size. Furthermore, SPA inserts initialization code for specific function parameters into the source code under test and supports model building and optimization algorithms to reduce the state space. We have successfully applied this optimized hybrid verification methodology to embedded software applications: Motorola's Powerstone Benchmark suite and a complex automotive industrial embedded software. The results show that our approach scales better than standalone software model checkers to reach deep state spaces.