Systematic Detection of Energy Regression and Corresponding Code Patterns in Java Projects

TL;DR

EnergyTrackr is a novel approach for automatically detecting energy regressions and related code anti-patterns in Java projects through empirical analysis of commit histories.

Contribution

It introduces EnergyTrackr, the first automated method to identify statistically significant energy regressions and anti-patterns at the commit level in software repositories.

Findings

Successfully identified energy regressions in 3,232 commits from Java projects.

Detected recurring anti-patterns like missing early exits and costly dependency upgrades.

Demonstrated the approach's effectiveness in aiding developers to optimize energy consumption.

Abstract

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.