As data centers worldwide consume more power than ever, lowering the energy consumption of software is increasingly important. Software energy testing is often unclear due to a lack of comparable baselines. In this paper, we look at the use of regression testing to alleviate some of the struggles with energy testing. We introduce E-Compare, a tool designed to identify energy regressions in software updates by comparing the energy consumption of different versions of the same project. E-Compare is cloud-based, fully automated, and can be implemented in any project with just three lines of code. To validate its effectiveness, we applied E-Compare to thirteen real-world projects, ranging from long-established projects to newer, active ones. Over 700 code changes have been tested. Our findings indicate that energy regression testing can identify energy regressions missed by developers. Some of the indicated energy regressions could be traced back to specific code changes, confirming the tool’s accuracy and relevance. However, the tool’s usability varies significantly depending on the project, and unexpected energy regressions are relatively rare.

Continuous Integration (CI) has become a cornerstone of modern software development, gaining widespread adoption due to its ability to facilitate frequent and dependable code integration. However, its benefits are offset by high computational costs and energy consumption, particularly in the build phase. With its growing popularity, it is crucial to reflect on the efficiency of the CI process. This thesis proposes a novel framework to optimise energy consumption in the build jobs of CI pipelines, with primary focus on minimising compilation workload. Leveraging static dependency analysis and commit information, the framework introduces guided partial compilation, targeting only files affected by changes. The results demonstrate its ability to maintain CI reliability while significantly reducing energy consumption in real-world projects, with a 22% reduction of energy consumption in compilation-only experiments, and up to 63% energy savings in experiments that extrapolate the effects of partial compilation across the rest of the build job. The contributions in this research offer a stepping stone toward the imperative establishment of sustainable standards within the CI practice.

Integrating Artificial Intelligence (AI) into software systems has significantly enhanced their capabilities while escalating energy demands. Ensemble learning, combining predictions from multiple models to form a single prediction, intensifies this problem due to cumulative energy consumption.

This paper presents a novel approach to model selection that addresses the challenge of balancing the accuracy of AI models with their energy consumption in a live AI ensemble system. We explore how reducing the number of models or improving the efficiency of model usage within an ensemble during inference can reduce energy demands without substantially sacrificing accuracy.

This study introduces and evaluates two model selection strategies, Static and Dynamic, for optimizing ensemble learning systems' performance while minimizing energy usage. Our results demonstrate that the Static strategy improves the F1 score beyond the baseline, reducing average energy usage from 100% from the full ensemble to 62%.
The Dynamic strategy further enhances F1 scores, while using on average 76% compared to 100% of the full ensemble.

Moreover, we propose an approach that balances accuracy with resource consumption, significantly reducing energy usage without substantially impacting accuracy. This method decreased the average energy usage of the Static strategy from approximately 62% to 14%, and for the Dynamic strategy, from around 76% to 57%.

Our field study of Green AI using an operational AI system developed by a large professional services provider shows the practical applicability of adopting energy-conscious model selection strategies in live production environments.

Containerization has become a fundamental component of software development and the continuous integration and continuous delivery (CI/CD) pipeline. However, the energy overhead of containerization solutions such as Docker results not only in higher costs but also has environmental consequences. Currently, the energy efficiency of containers is often neglected, since developers mainly focus on small image size and time efficiency. Furthermore, there is also a lack of understanding about the impact of base images on the container during runtime. Therefore, the objective of this thesis is to assess the impact of base image selection on the energy efficiency of containerized workloads. This is done by running an empirical study, in which we conduct experiments with a diverse set of real-world workloads and base images. The results of this study show that the base image can have a significant impact on the energy efficiency of the container. However, the magnitude of this impact in practice depends on the workload. For some workloads (i.e. databases) this impact is more significant in practice than for others (i.e. gaming servers). While there is no single best or worst image across all workloads, Alpine is often the least energy-efficient option. Furthermore, the execution time of the container is only correlated to the energy consumption for CPU-intensive workloads. Therefore, besides image size and time efficiency, it is important to consider energy efficiency in the selection criteria as well when considering sustainable practices in software engineering.