Systematic Detection of Energy Regression and Corresponding Code Patterns in Java Projects
For software developers, it provides an automated method to detect energy regressions at the commit level and identify associated code anti-patterns, addressing a gap in green software engineering.
EnergyTrackr detects energy regressions across commits in Java projects by identifying statistically significant energy changes, and links them to code anti-patterns like missing early exits or costly dependency upgrades. Evaluated on 3,232 commits from three projects, it effectively identifies significant energy changes.
Green software engineering is emerging as a crucial response to information technology's rising energy impact, especially in continuous development. However, there remain challenges in devising automated methods for identifying energy regressions across commits and their associated code change patterns. In particular, little effort has been put into automatically detecting regressions at the commit level by identifying statistically significant changes in energy consumption. In this paper, we introduce EnergyTrackr, an approach designed to detect energy regressions across multiple commits that can then be used to identify code anti-patterns potentially contributing to the increase of software energy consumption over time. We describe our empirical evaluation, including repository mining and source code analysis, made on 3,232 commits from three Java projects, and show the approach's ability to identify significant energy changes. We also highlight recurring anti-patterns such as missing early exits or costly dependency upgrades. We expect EnergyTrackr to assist developers in accurately monitoring energy regressions and improvements within their projects, identifying code anti-patterns, and helping them optimize their source code to reduce software energy consumption.