Generative artificial intelligence (GenAI) tools have seen rapid adoption among software developers. While adoption rates in the industry are rising, the underlying factors influencing the effective use of these tools, including the depth of interaction, organizational constraints, and experience-related considerations, have not been thoroughly investigated. This issue is particularly relevant in environments with stringent regulatory requirements, such as Germany, where practitioners must address the GDPR and the EU AI Act while balancing productivity gains with intellectual property considerations. Despite the significant impact of GenAI on software engineering, to the best of our knowledge, no empirical study has systematically examined the adoption dynamics of GenAI tools within the German context. To address this gap, we present a comprehensive mixed-methods study on GenAI adoption among German software engineers. Specifically, we conducted 18 exploratory interviews with practitioners, followed by a developer survey with 109 participants. We analyze patterns of tool adoption, prompting strategies, and organizational factors that influence effectiveness. Our results indicate that experience level moderates the perceived benefits of GenAI tools, and productivity gains are not evenly distributed among developers. Further, organizational size affects both tool selection and the intensity of tool use. Limited awareness of the project context is identified as the most significant barrier. We summarize a set of actionable implications for developers, organizations, and tool vendors seeking to advance artificial intelligence (AI) assisted software development.

翻译：生成式人工智能（GenAI）工具已在软件开发人员中迅速得到采用。尽管行业采用率正在上升，但影响这些工具有效使用的潜在因素，包括交互深度、组织约束以及与经验相关的考量，尚未得到深入探究。这一问题在监管要求严格的环境中尤为突出，例如德国，从业人员必须在应对《通用数据保护条例》（GDPR）和《欧盟人工智能法案》（EU AI Act）的同时，平衡生产力提升与知识产权考量。尽管GenAI对软件工程产生了重大影响，但据我们所知，目前尚无实证研究系统性地考察德国背景下GenAI工具的采用动态。为填补这一空白，我们针对德国软件工程师群体开展了一项全面的混合方法研究，以探究GenAI的采用情况。具体而言，我们进行了18次探索性从业人员访谈，随后开展了一项包含109名参与者的开发者调查。我们分析了工具采用模式、提示策略以及影响有效性的组织因素。结果表明，经验水平会调节GenAI工具的感知收益，而生产力提升在开发者之间并非均匀分布。此外，组织规模同时影响工具选择和使用强度。项目上下文认知不足被确定为最显著的障碍。我们为寻求推进人工智能（AI）辅助软件开发的开发者、组织和工具供应商总结了一系列可操作的启示。