The DevSafeOps dilemma: A systematic literature review on rapidity in safe autonomous driving development and operation

IF 4.1 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software Pub Date : 2025-07-11 DOI:10.1016/j.jss.2025.112555

Ali Nouri , Beatriz Cabrero-Daniel , Fredrik Törner , Christian Berger

引用次数: 0

Abstract

Developing autonomous driving (AD) systems is challenging due to the complexity of the systems and the need to assure their safe and reliable operation. The widely adopted approach of DevOps seems promising to support the continuous technological progress in AI and the demand for fast reaction to incidents, which necessitate continuous development, deployment, and monitoring. We present a systematic literature review meant to identify, analyse, and synthesise a broad range of existing literature related to usage of DevOps in autonomous driving development. Our results provide a structured overview of challenges and solutions, arising from applying DevOps to safety-related AI-enabled functions. Our results indicate that there are still several open topics to be addressed to enable safe DevOps for the development of safe AD.

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DevSafeOps困境：关于安全自动驾驶开发和运行的快速性的系统文献综述

由于系统的复杂性以及确保其安全可靠运行的需求，开发自动驾驶（AD）系统具有挑战性。广泛采用的DevOps方法似乎有希望支持人工智能的持续技术进步和对事件的快速反应需求，这需要持续的开发、部署和监控。我们提出了一个系统的文献综述，旨在识别、分析和综合与DevOps在自动驾驶开发中的使用有关的广泛的现有文献。我们的研究结果提供了一个结构化的挑战和解决方案概述，这些挑战和解决方案来自于将DevOps应用于与安全相关的ai功能。我们的研究结果表明，为了使安全的DevOps开发安全的AD，仍然有几个开放的主题需要解决。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Systems and Software 工程技术-计算机：理论方法

CiteScore

8.60

自引率

5.70%

发文量

193

审稿时长

16 weeks

期刊介绍： The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to: •Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution •Agile, model-driven, service-oriented, open source and global software development •Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems •Human factors and management concerns of software development •Data management and big data issues of software systems •Metrics and evaluation, data mining of software development resources •Business and economic aspects of software development processes The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.