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

2026-04-21

AI Summary

This work addresses the challenge of automatically detecting energy-regression-inducing code changes at the commit level in Java projects. We propose EnergyTrackr, the first approach that leverages statistical significance testing to automatically identify commits causing substantial energy consumption increases and precisely links them to specific energy-inefficient code anti-patterns—such as missing early-exit conditions or costly dependency upgrades. By integrating repository mining, static source code analysis, and empirical energy measurement, EnergyTrackr is evaluated on 3,232 real-world Java commits. The results demonstrate its effectiveness in pinpointing statistically significant energy regressions and recurring anti-patterns, thereby offering developers actionable insights for energy-aware code optimization.

Technology Category

• Computer Vision: Motion & Tracking
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• energy regression
• code patterns
• green software engineering
• commit-level analysis
• software energy consumption

Innovation

• energy regression detection
• commit-level analysis
• code anti-patterns
• green software engineering
• EnergyTrackr