EnCoDe: Energy Estimation of Source Code At Design-Time

TL;DR

EnCoDe introduces a novel design-time methodology for fine-grained energy estimation of small code blocks, enabling early optimization without runtime profiling.

Contribution

It presents PowerLens for reliable sub-millisecond energy measurements and machine learning models trained on a large dataset for accurate energy prediction.

Findings

Achieved R^2 = 0.75 in energy regression models.

Classifiers reached 80.6% accuracy in identifying energy hotspots.

Provided the first fine-grained dataset linking static code features to energy consumption.

Abstract

Energy efficiency has emerged as a vital attribute of software quality, with significant implications for both environmental sustainability and operational costs. However, existing profiling tools operate only at runtime and coarse granularity, typically capturing energy at the process or method level. Such tools fail to expose how small code blocks, such as functions, loops, and conditionals, contribute to energy consumption, preventing developers from reasoning about and comparing the energy efficiency of programming constructs during design-time. To address this gap, we propose EnCoDe, a methodology for fine-grained, design-time energy estimation, with the following key contributions: (1) PowerLens, a novel measurement methodology that achieves reliable sub-millisecond energy readings for small code blocks; (2) Extensive empirical study on code blocks extracted from over 18,000 Python programs, uncovering linear and non-linear relationships between energy consumption and static code features such as structural, complexity, density, and contextual characteristics, resulting in a first-of-its-kind fine-grained dataset; and (3) Predictive modeling, in which machine learning models are trained on these features to accurately estimate and classify block-level energy consumption at design-time. Our results demonstrate stable, reproducible block-level estimations, with regressors achieving R^2 = 0.75 and classifiers achieving 80.6% accuracy in identifying energy hotspots, enabling developers to localize and address inefficient code regions early in the development process without execution.