Accelerating the Design Phase: Towards DevSafeOps for Autonomous Driving Software

Licentiate thesis, 2024

Background: The safety of Autonomous Driving (AD) remains a barrier to its widespread adoption, as evidenced by recent incidents. Factors such as the complex environment, evolving technologies, and shifting regulatory and customer requirements necessitate continuous monitoring and improvement of AD software. This is a process that may favor software and system engineering supported by DevOps. The iterative DevOps process is crucial, serving two purposes: satisfying customer demands through continuous improvement of the function and providing a framework for timely responses to unknown bugs or incidents. However, any update to the software must follow rigorous safety processes prescribed by standards, regulations, or the state of the art in industry. Incorporating these safety activities into the DevOps forms an iterative process called DevSafeOps. These necessary activities, although vital for safety assurance, inherently lead to a compromise in rapidity.
Research Goal: In this work, we initially identify the challenges in the rapid DevSafeOps in AD development and then explore existing solutions. Subsequently, we propose two approaches for accelerating the primary activities in the AD development, which are requirements engineering and safety analysis. Methods: To address each research objective, diverse research methods are utilized. Interview studies and a systematic literature review are conducted to identify the challenges and research gaps. Then, design science, interview study, and a case study are employed for the proposed approaches.
Results: Initially, the challenges and research gaps related to each essential activity for the safety of AD are identified (Papers A and B). The proposed solutions in literature are identified and mapped to the challenges (Paper B). Then, two approaches are proposed for the rapidity of safety analysis, which is the initial step in the development. We adapt System Theoretic Process Analysis (STPA) for distributed development within automotive system engineering, which is our suggestion to approach the first challenge (Paper C). As an alternative approach, a Large Language Model (LLM)-based hazard analysis risk assessment prototype is developed and evaluated to enable automation (Papers D and E).
Conclusions: There are multiple challenges in achieving rapid DevSafeOps in AD development. The design phase, as a stepping stone of development, was underexplored with respect to methods for rapid updates in its artifacts. In one approach, we propose adapting STPA for multiparty distributed development to increase the speed of DevSafeOps. Subsequently, we explore the possibility of using LLMs to perform design phase activities with reduced engineers’ involvement. These two proposed approaches have the potential to contribute to an increase in speed in the design phase, one by enabling distributed development, and the other by automation.