Pith. sign in

REVIEW 5 cited by

Estimating the Energy Footprint of Software Systems: a Primer

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.11611 v2 pith:NNGNPV57 submitted 2024-07-16 cs.SE cs.PF

Fernando Castor This is my paper · ORCID
classification cs.SEcs.PF
keywords softwareenergyfootprintareabasicconductingdevelopmentgreen
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

In Green Software Development, quantifying the energy footprint of a software system is one of the most basic activities. This documents provides a high-level overview of how the energy footprint of a software system can be estimated to support Green Software Development. We introduce basic concepts in the area, highlight methodological issues that must be accounted for when conducting experiments, discuss trade-offs associated with different estimation approaches, and make some practical considerations. This document aims to be a starting point for researchers who want to begin conducting work in this area.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Empirical Study on the Performance and Energy Usage of Compiled Python Code

    cs.PL 2025-05 conditional novelty 6.0 of 10

    On seven single-threaded Computer Language Benchmarks Game programs with no third-party libraries, Codon, PyPy, and Numba cut average execution time and energy by roughly 86 to 95% compared with CPython, while Nuitka ...

  2. Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy

    cs.SE 2024-11 conditional novelty 6.0 of 10

    Larger language models do not reliably deliver higher accuracy on software tasks, and quantized large models can outperform full-precision medium models on energy and accuracy together.

  3. Generating Energy-Efficient Code via Large-Language Models -- Where are we now?

    cs.SE 2025-09 conditional novelty 5.0 of 10

    Across 363 solutions on three hardware platforms, no LLM consistently matched a green software expert's energy efficiency, though some LLM-prompt combinations beat ordinary human code.

  4. Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time

    cs.SE 2026-07 conditional novelty 4.0 of 10

    Static code metrics yield near-zero predictive power for Java method energy; adding execution time raises R² to 0.46, showing energy is dominated by runtime behavior.

  5. Strategies to Measure Energy Consumption Using RAPL During Workflow Execution on Commodity Clusters

    cs.DC 2025-05 conditional novelty 4.0 of 10

    Four energy measurement methods were compared on Kubernetes with Nextflow; shell-script and plugin approaches are effective, but concurrent tasks require heuristics for per-task attribution.

Pith tools