The energy consumption of software is an increasing concern as the use of mobile applications, embedded systems, and data center-based services expands. While research in green software engineering is correspondingly increasing, little is known about the current practices and perspectives of software engineers in the field. This paper describes the first empirical study of how practitioners think about energy when they write requirements, design, construct, test, and maintain their software. We report findings from a quantitative, targeted survey of 464 practitioners from ABB, Google, IBM, and Microsoft, which was motivated by and supported with qualitative data from 18 in-depth interviews with Microsoft employees. The major findings and implications from the collected data contextualize existing green software engineering research and suggest directions for researchers aiming to develop strategies and tools to help practitioners improve the energy usage of their applications.

Reducing the energy usage of software is becoming more important in many environments, in particular, batterypowered mobile devices, embedded systems and data centers. Recent empirical studies indicate that software engineers can support the goal of reducing energy usage by making design and implementation decisions in ways that take into consideration how such decisions impact the energy usage of an application. However, the large number of possible choices and the lack of feedback and information available to software engineers necessitates some form of automated decision-making support. This paper describes the first known automated support for systematically optimizing the energy usage of applications by making code-level changes. It is e↵ective at reducing energy usage while freeing developers from needing to deal with the low-level, tedious tasks of applying changes and monitoring the resulting impacts to the energy usage of their application. We present a general framework, SEEDS, as well as an instantiation of the framework that automatically optimizes Java applications by selecting the most energye cient library implementations for Java's Collections API. Our empirical evaluation of the framework and instantiation show that it is possible to improve the energy usage of an application in a fully automated manner for a reasonable cost.

Software engineers make decisions about the design of the software they are creating on a daily basis. These decisions may impact the application in terms of efficiency, usability, flexibility, etc. Different competing design decisions are therefore often evaluated in terms of their projected impact on quality metrics prior to implementation. Recently energy has become a concern for software systems, ranging from mobile devices to large data centers. Additionally, it has been recognized that the software executing on a computing device can have a significant impact on the device's energy consumption. This raises the obvious question of whether or not it is possible to reduce the energy consumption of a software system by the means of software design decisions.This work examines how the use of different servers impacts the energy consumption of a web application. Through a controlled empirical experiment we have discovered several important findings in this regard. The results indicate that the energy consumption of a web application can vary greatly depending on the web server used to handle its requests. Furthermore, different web servers are more or less energy efficient depending on which web application features are being executed. The paper details an analysis of the results of the experiment.

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