将能源效率集成到软件开发中：开发者的观点和需求
Integrating Energy Efficiency into Software Development: Developer Perspectives and Requirements
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中文总结 AI 辅助
研究软件开发人员对能源效率的看法及对相关工具的需求障碍，通过访谈和分析得出，能源效率在日常开发中作用不明确，节能多通过性能优化间接实现，还明确了障碍与工具支持要求，为相关工具设计提供见解。
中文摘要 AI 辅助
在信息通信技术能源足迹不断增长的背景下，行业优化主要集中在硬件，软件对能源消耗的影响常被忽视。虽研究已开发出优化软件能耗的技术方法，但日常开发实践中的采用率仍有限。本研究调查软件开发人员在日常工作中如何看待能源效率，以及他们对支持能源感知开发的人工智能辅助工具提出了哪些要求和障碍。作为欧洲GreenCode项目的一部分，对专业软件开发人员进行了十次半结构化访谈，并采用Mayring方法进行定性内容分析。随后，部分确定的要求通过技术接受模型的视角进行了解释。结果表明，能源效率在日常开发活动中很少发挥明确作用。相反，节能通常通过性能优化间接实现。确定的明确考虑能源效率的障碍包括意识有限和对及时交付的强烈关注。访谈还揭示了对实用工具支持的要求，如可行的优化建议和无缝集成到常见开发环境中。此外，对人工智能辅助优化工具的接受程度在很大程度上取决于数据使用的透明度、与工具本身能耗相比的实际节能效果，以及所用训练数据的披露情况。本研究从开发者角度对能源感知软件开发工具的要求做出了贡献，并为设计符合实际开发实践的人工智能辅助解决方案提供了见解。
英文摘要
In the context of the growing energy footprint of information and communication technology, industry optimization efforts have primarily focused on hardware, while the impact of software on energy consumption is often overlooked. Although technical approaches for optimizing software energy consumption have been developed in research, their adoption in everyday development practice remains limited. This study investigates how software developers perceive energy efficiency in their daily work and which requirements and barriers they formulate for AI-assisted tools supporting energy-aware development. As part of the European GreenCode project, ten semi-structured interviews with professional software developers were conducted and analyzed using qualitative content analysis following Mayrings methodology. The identified requirements were subsequently partly interpreted through the lens of the Technology Acceptance Model. The results indicate that energy efficiency rarely plays an explicit role in daily development activities. Instead, energy savings are typically achieved indirectly through performance optimization. Identified barriers to the explicit consideration of energy efficiency include limited awareness and a strong focus on timely delivery. The interviews further revealed requirements for practical tool support, such as actionable optimization suggestions and seamless integration into common development environments. Furthermore, the acceptance of AI-assisted optimization tools strongly depends on transparency regarding the use of data, the actual energy savings compared to the energy consumption of the tool itself, and the disclosure of the training data used. This study contributes a developer-centered perspective on requirements for energy-aware software development tools and provides insights for designing AI-assisted solutions that align with real-world development practices.
