Model-Checking in the Loop Model-Based Testing for Automotive Operating Systems

TL;DR

This paper introduces MCIL-MBT, an integrated model-based approach combining model-checking and testing to verify automotive operating systems, effectively identifying flaws beyond traditional methods.

Contribution

The paper presents a novel iterative verification and testing framework for automotive OS using formal models and model-checking, enhancing flaw detection capabilities.

Findings

Successfully identified previously undetected flaws in automotive OS

Validated the effectiveness of MCIL-MBT with industry partners

Improved reliability of automotive operating systems through formal verification

Abstract

While vehicles have primarily been controlled through mechanical means in years past, an increasing number of embedded control systems are being installed and used, keeping pace with advances in electronic control technology and performance. Automotive systems consist of multiple components developed by a range of vendors. To accelerate developments in embedded control systems, industrial standards such as AUTOSAR are being defined for automotive systems, including the design of operating system and middleware technologies. Crucial to ensuring the safety of automotive systems, the operating system is foundational software on which many automotive applications are executed. In this paper, we propose an integrated model-based method for verifying automotive operating systems; our method is called Model-Checking in the Loop Model-Based Testing (MCIL-MBT). In MCIL-MBT, we create a model that formalizes specifications of automotive operating systems and verifies the specifications via model-checking. Next, we conduct model-based testing with the verified model to ensure that a specific operating system implementation conforms to the model. These verification and testing stages are iterated over until no flaws are detected. Our method has already been introduced to an automotive system supplier and an operating system vendor. Through our approach, we successfully identified flaws that were not detected by conventional review and testing methods.