Approximate Computing for Build Jobs in Continuous Integration

Abstract

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.