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.

DevSafeOps

DevOps

Safety

STPA

Hazard Analysis Risk Assessment

Large Language Model

Requirements Engineering

Autonomous Vehicles

Styrbord Lecture Hall, On-site on campus Lindholmen, Gothenburg
Opponent: Prof. Philip Koopman, Carnegie Mellon University, US

Author

Ali Nouri

Software Engineering 1

An Industrial Experience Report about Challenges from Continuous Monitoring, Improvement, and Deployment for Autonomous Driving Features

Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022,;(2022)

Paper in proceeding

A. Nouri, B. Cabrero-Daniel, F. To ̈rner, C. Berger, The DevSafeOps Dilemma: A Systematic Literature Review on Rapidity in Safe Autonom- ous Driving Development and Operation Submitted, under review in Journal of Systems and Software.

On STPA for Distributed Development of Safe Autonomous Driving: An Interview Study

Proceedings - 2023 49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023,;(2023)

Paper in proceeding

Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models

PROCEEDINGS 2024 IEEE/ACM 3RD INTERNATIONAL CONFERENCE ON AI ENGINEERING-SOFTWARE ENGINEERING FOR AI, CAIN 2024,;(2024)p. 172-177

Paper in proceeding

Engineering Safety Requirements for Autonomous Driving with Large Language Models

Proceedings of the IEEE International Conference on Requirements Engineering,;(2024)p. 218-228

Paper in proceeding

Areas of Advance

Information and Communication Technology

Transport

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Driving Forces

Innovation and entrepreneurship

Subject Categories

Software Engineering

Publisher

Chalmers

Styrbord Lecture Hall, On-site on campus Lindholmen, Gothenburg

Online

Opponent: Prof. Philip Koopman, Carnegie Mellon University, US

More information

Latest update

9/16/2024