Optimizing Regression Test Suites for Automotive Embedded Systems: A Risk-Based Approach

Authors- Vikas Gore, Pranjal Dhengale

Abstract-– The In the realm of automotive embedded systems, ensuring the reliability and safety of software is paramount. Regression testing plays a critical role in maintaining software quality by verifying that recent code changes have not adversely affected existing functionalities. However, the extensive nature of regression test suites can lead to significant time and resource consumption. This paper presents a risk-based approach to optimizing regression test suites for automotive embedded systems. By prioritizing test cases based on the risk associated with different software components, we aim to enhance testing efficiency while maintaining high standards of safety and reliability. Our methodology involves identifying high-risk areas through a combination of historical data analysis, expert judgment, and automated risk assessment tools. The proposed approach is validated through a series of case studies, demonstrating its effectiveness in reducing test execution time and resource usage without compromising the detection of critical defects. Automotive embedded systems are growing in complexity, requiring efficient regression testing to meet safety standards like ISO 26262. Traditional approaches often result in redundant test executions, increasing time and resource usage. This paper proposes a risk-based method to optimize regression test suites by prioritizing and minimizing test cases based on software criticality, change impact, and historical fault data. Risk is assessed using a scoring model incorporating ASIL, change frequency, and defect density. Applied to a simulated ECU, the approach shows improved testing efficiency while maintaining fault detection and system reliability. It supports faster development cycles and robust software delivery, especially in CI/CD and OTA update scenarios.

DOI: /10.61463/ijset.vol.13.issue2.314