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.

Audio fingerprinting is one of the standard solutions for music identification. The underlying technique is designed to be robust to signal degradation such that music can be identified despite its presence. One of the newly emerged applications of a possibly challenging nature is music identification in movies. This paper examines the audio fingerprinting framework Dejavu by evaluating its performance against an existing benchmark created for the context of music identification in movies. The results show that Dejavu’s performance matches the expectations derived from the implementation and previous testing, and can be reconfigured to improve the performance in terms of the benchmark.