🤖 AI Summary
This study addresses the insufficient attention to energy consumption in contemporary software development and the lack of energy-efficiency tools aligned with developers’ practical needs. Through semi-structured interviews and Mayring’s qualitative content analysis, integrated with the Technology Acceptance Model (TAM), the research systematically investigates developers’ awareness of software energy efficiency, the barriers they encounter in practice, and their requirements for AI-assisted tools. From the developer perspective, the study identifies core design principles for effective energy-efficiency tooling: delivering actionable energy-saving recommendations, enabling low-intrusion integration into existing development workflows, and ensuring transparent disclosure of data usage and quantified energy savings. These findings offer a user-centered design pathway for green software engineering, substantially enhancing both the perceived usefulness and adoption likelihood of energy-efficiency tools among developers.
📝 Abstract
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.