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arxiv: 2604.19373 · v1 · submitted 2026-04-21 · 💻 cs.SE

Recognition: unknown

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

Fran\c{c}ois Bechet , J\'er\^ome Maquoi , Lu\'is Cruz , Beno\^it Vanderose , Xavier Devroey
Authors on Pith no claims yet

Pith reviewed 2026-05-10 02:52 UTC · model grok-4.3

classification 💻 cs.SE
keywords energy regressioncode anti-patternsJava projectssoftware energy consumptioncommit analysisgreen software engineeringEnergyTrackrrepository mining
0
0 comments X

The pith

EnergyTrackr detects energy regressions across Java commits and connects them to recurring code anti-patterns.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents EnergyTrackr as a method to automatically spot statistically significant rises in energy use when code changes are committed. A sympathetic reader would care because continuous development makes it hard to notice gradual energy increases without such tracking. The approach mines repositories, measures consumption, applies statistical tests, and analyzes source code to flag patterns like missing early exits or expensive dependency updates. Evaluation on over three thousand commits from three projects shows it can surface these changes reliably enough to support ongoing monitoring.

Core claim

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. Our empirical evaluation on 3,232 commits from three Java projects demonstrates the approach's ability to identify significant energy changes and highlights recurring anti-patterns such as missing early exits or costly dependency upgrades.

What carries the argument

EnergyTrackr, a pipeline that combines repository mining, repeated energy measurements at commit granularity, statistical significance checks for consumption differences, and manual source-code review to map regressions to anti-patterns.

If this is right

  • Developers gain a way to track energy trends alongside functional changes in their commit history.
  • Identified anti-patterns can guide targeted refactoring to lower long-term consumption.
  • The method supports integration into green software engineering workflows for continuous improvement.
  • Recurring patterns across projects can inform broader guidelines for energy-efficient coding.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same commit-level measurement approach could be adapted to track other non-functional properties such as memory usage or execution time.
  • If anti-patterns prove consistent across more languages and domains, automated refactorings could be developed to prevent them.
  • Larger-scale application might reveal project-specific energy budgets that teams could use for planning.

Load-bearing premise

Accurate, repeatable energy measurements can be taken at individual commits so that statistically significant differences truly reflect code-induced regressions rather than measurement noise or unrelated factors.

What would settle it

Run the tool on a set of commits where energy-neutral changes are deliberately inserted; if it reports many false regressions, or if it misses known energy-increasing changes, the detection reliability is refuted.

Figures

Figures reproduced from arXiv: 2604.19373 by Beno\^it Vanderose, Fran\c{c}ois Bechet, J\'er\^ome Maquoi, Lu\'is Cruz, Xavier Devroey.

Figure 1
Figure 1. Figure 1: Excerpt of the different plots generated for [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Cusum and change point detection plots for the [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces EnergyTrackr, an approach for detecting energy regressions across commits in Java projects via repository mining, per-commit energy profiling, and statistical testing, followed by identification of associated code anti-patterns. It evaluates the method on 3,232 commits from three Java projects and reports the ability to detect significant energy changes along with recurring patterns such as missing early exits and costly dependency upgrades.

Significance. If the underlying energy measurements are shown to be low-variance and repeatable, EnergyTrackr could provide a practical contribution to green software engineering by automating regression detection at commit granularity within CI pipelines. The scale of the evaluation (thousands of commits) is a positive aspect that could support identification of actionable anti-patterns, but only once the measurement and statistical pipeline is fully specified and validated.

major comments (2)
  1. [Empirical evaluation] Empirical evaluation section: the paper asserts detection of 'statistically significant' energy changes across 3,232 commits but supplies no information on the energy measurement method (e.g., hardware sensors, RAPL, or external tools), number of repetitions per commit, controls for JVM warm-up/GC/OS noise, or the exact statistical test and significance threshold applied. This directly undermines the central claim, as the skeptic concern about measurement noise at commit granularity cannot be assessed without these details.
  2. [Anti-pattern identification] Anti-pattern identification step (following regression detection): the linkage between flagged regressions and specific code patterns (missing early exits, dependency upgrades) is presented as a contribution, yet the manuscript does not describe the code-change analysis procedure or provide evidence that the patterns are causally tied to the energy delta rather than coincidental.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for the constructive feedback, which highlights important gaps in methodological transparency. We address each major comment below and will revise the manuscript to incorporate the requested details and clarifications.

read point-by-point responses

  1. Referee: [Empirical evaluation] Empirical evaluation section: the paper asserts detection of 'statistically significant' energy changes across 3,232 commits but supplies no information on the energy measurement method (e.g., hardware sensors, RAPL, or external tools), number of repetitions per commit, controls for JVM warm-up/GC/OS noise, or the exact statistical test and significance threshold applied. This directly undermines the central claim, as the skeptic concern about measurement noise at commit granularity cannot be assessed without these details.

    Authors: We acknowledge that the current manuscript does not supply these essential methodological details, which prevents proper evaluation of measurement reliability and statistical validity. In the revised version we will add a new subsection titled 'Energy Measurement and Statistical Analysis' that explicitly describes: use of Intel RAPL via the jRAPL library for CPU package energy; 10 repetitions per commit with mean and standard deviation reported; JVM warm-up of 2000 iterations plus GC disabling via flags; isolated execution on dedicated hardware with OS noise mitigation; and application of the Wilcoxon signed-rank test with p < 0.05 after Bonferroni correction. We will also include a table of per-project energy variance to address repeatability concerns. revision: yes

  2. Referee: [Anti-pattern identification] Anti-pattern identification step (following regression detection): the linkage between flagged regressions and specific code patterns (missing early exits, dependency upgrades) is presented as a contribution, yet the manuscript does not describe the code-change analysis procedure or provide evidence that the patterns are causally tied to the energy delta rather than coincidental.

    Authors: We agree that the anti-pattern step lacks procedural description and supporting evidence. The revised manuscript will include a dedicated 'Code Pattern Analysis' subsection explaining that we manually inspected unified diffs of all commits flagged with significant energy increases, applying a lightweight taxonomy derived from prior green-software literature to label changes (e.g., insertion of early returns, library version bumps). We will report frequencies (e.g., 18 % of regressions linked to missing early exits) and provide three concrete commit examples with before/after energy deltas. We will frame the patterns as 'recurring associations' rather than causal claims and explicitly note the absence of isolated micro-benchmark validation as a limitation. revision: partial

Circularity Check

0 steps flagged

No circularity in empirical energy regression detection pipeline

full rationale

The paper introduces EnergyTrackr as an empirical method that mines repositories, profiles energy consumption per commit, applies statistical tests for significant changes, and then inspects code for anti-patterns. This chain relies on external measurements and data analysis rather than any self-definitional loop, fitted parameter renamed as prediction, or load-bearing self-citation. No equations or derivations are presented that reduce the claimed detection capability to the method's own inputs by construction; the evaluation on 3,232 commits stands as independent evidence of the approach's behavior.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that energy can be measured at commit granularity with sufficient precision for statistical significance testing; no free parameters or invented entities are mentioned in the abstract.

axioms (1)
  • domain assumption Energy consumption of Java programs can be measured accurately enough at the commit level to detect statistically significant regressions
    The entire detection pipeline depends on this measurement capability being reliable.

pith-pipeline@v0.9.0 · 5498 in / 1223 out tokens · 36111 ms · 2026-05-10T02:52:53.708353+00:00 · methodology

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Reference graph

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