A new transformation is underway in software engineering, driven by the rapid adoption of generative AI in development workflows. Similar to how version control systems once automated manual coordination, AI tools are now beginning to automate many aspects of programming. For embedded software engineering organizations, however, this marks their first experience integrating AI into safety-critical and resource-constrained environments. The strict demands for determinism, reliability, and traceability pose unique challenges for adopting generative technologies. In this paper, we present findings from a qualitative study with ten senior experts from four companies who are evaluating generative AI-augmented development for embedded software. Through semi-structured focus group interviews and structured brainstorming sessions, we identified eleven emerging practices and fourteen challenges related to the orchestration, responsible governance, and sustainable adoption of generative AI tools. Our results show how embedded software engineering teams are rethinking workflows, roles, and toolchains to enable a sustainable transition toward agentic pipelines and generative AI-augmented development.

翻译：软件工程领域正在经历一场新的变革，其驱动力是生成式人工智能在开发工作流程中的迅速普及。类似于版本控制系统曾经自动化了人工协调过程，人工智能工具如今正开始自动化编程的诸多方面。然而，对于嵌入式软件工程组织而言，这标志着他们首次将人工智能集成到安全关键和资源受限的环境中。对确定性、可靠性和可追溯性的严格要求，为采用生成式技术带来了独特的挑战。本文展示了一项定性研究的结果，该研究涉及来自四家公司的十位资深专家，他们正在评估用于嵌入式软件的生成式人工智能增强开发。通过半结构化焦点小组访谈和结构化头脑风暴会议，我们识别了与生成式人工智能工具的编排、负责任治理和可持续采用相关的十一项新兴实践和十四项挑战。我们的研究结果表明，嵌入式软件工程团队正在重新思考工作流程、角色和工具链，以实现向智能体化流水线和生成式人工智能增强开发的可持续转型。