Implementation of automated test case generation and fault classification by mutation testing for adaptive cruise control system

Abstract

Adaptive Cruise Control (ACC) systems are vital components of modern vehicles, designed to enhance safety and driving comfort by automatically maintaining safe distances from other vehicles. As these systems grow more complex, ensuring their reliability through effective software testing becomes increasingly critical. Traditional testing approaches often fall short in covering the wide range of real-world driving scenarios, and manual test case development is time-consuming and prone to oversight. This research proposes an integrated framework that combines automated test case generation with mutation testing to improve the accuracy and efficiency of fault detection and classification in ACC software. The methodology involves generating test cases automatically from the ACC system model using tools such as Simulink Design Verifier. Mutation testing is applied by injecting artificial faults into the software to evaluate the strength of the test suite. Additionally, a fault classification mechanism is implemented to categorize detected errors, aiding in faster debugging and root cause analysis. Experimental evaluation on a simulated ACC model demonstrated that the integrated approach achieved a mutation score of 87%, significantly outperforming manual testing methods. Furthermore, over 80% of the detected faults were accurately classified into predefined categories. The findings confirm that the proposed framework enhances software test coverage and diagnostic capability while reducing manual effort. This research contributes to the field of automotive software engineering by presenting a scalable, automated solution for validating safety-critical control systems. It supports the advancement of secure, reliable autonomous driving technologies and aligns with industry standards such as ISO 26262.