[Paper] How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study

Source: arXiv - 2512.24636v1

Overview

The paper investigates whether AI‑driven coding assistants (e.g., GitHub Copilot, Code Llama) actually think about energy consumption when they suggest code changes. By mining a public pull‑request (PR) dataset, the authors identified 216 PRs that explicitly mention “energy” and analyzed the kinds of optimizations the agents propose. Their findings show that, despite the heavy energy cost of running these models, the agents can produce energy‑aware patches—though such patches are accepted less often because they may hurt code maintainability.

Key Contributions

• Empirical dataset: Extraction of 216 “energy‑explicit” PRs authored by AI coding agents from a large, publicly available repository.
• Taxonomy of energy‑aware work: A thematic classification (e.g., algorithmic refactoring, API substitution, hardware‑specific tuning) that captures how agents address energy concerns.
• Technique alignment analysis: Comparison of the agents’ suggested optimizations with established energy‑efficiency research, showing strong overlap.
• Acceptance study: Quantitative evidence that energy‑focused PRs have a lower merge rate, primarily due to perceived maintainability trade‑offs.
• Insight into SE 3.0: Demonstrates that AI‑assisted development can be energy‑conscious, a prerequisite for sustainable software engineering at scale.

Methodology

1. Data collection – The authors leveraged an existing open‑source PR dataset that tags the author of each PR. They filtered for PRs where the author field matches known AI coding agents.
2. Energy‑explicit identification – Using keyword searches (e.g., “energy”, “power”, “battery”) and manual validation, they isolated PRs that explicitly discuss energy impact.
3. Thematic analysis – Two researchers independently coded the PR descriptions and code diffs, iteratively refining categories until reaching a stable taxonomy of energy‑aware activities.
4. Technique mapping – Each identified optimization was mapped to recommendations from prior energy‑efficiency literature (e.g., algorithmic complexity reduction, lazy evaluation, hardware‑accelerated libraries).
5. Acceptance measurement – Merge status, review comments, and time‑to‑merge were extracted to compare energy‑focused PRs against a baseline of non‑energy PRs.

The approach balances quantitative mining with qualitative coding, making the results understandable even for developers unfamiliar with systematic literature reviews.

Results & Findings

• Energy‑aware PR prevalence: Only ~0.3 % of all AI‑generated PRs mention energy, indicating that agents rarely surface this concern without prompting.
• Taxonomy highlights: The most common categories were algorithmic refactoring (38 %), API substitution for lower‑power libraries (24 %), and hardware‑specific tuning (18 %).
• Alignment with research: 71 % of the suggested optimizations matched best‑practice guidelines from the energy‑efficiency literature, suggesting agents have internalized many proven techniques.
• Acceptance gap: Energy‑focused PRs were merged at a rate of 42 % versus 68 % for other AI‑generated PRs. Review comments frequently cited “maintainability” or “code readability” as concerns.
• Energy impact: In a subset of 30 PRs where the authors could run benchmarks, average power consumption dropped by 12 % after applying the agent’s changes, confirming real‑world benefits.

Practical Implications

• Tooling for sustainable CI/CD: Teams can integrate a lightweight “energy‑check” step that flags AI‑generated suggestions touching on power usage, prompting a manual review of maintainability trade‑offs.
• Prompt engineering: Developers can explicitly ask agents to “optimize for energy” or “preserve readability” to steer the model toward balanced solutions.
• Policy & governance: Organizations operating large data‑center‑hosted AI services can adopt guidelines that require energy impact statements for any AI‑generated code change.
• Hardware‑aware development: The taxonomy provides a ready‑made checklist (e.g., prefer SIMD‑friendly loops, avoid unnecessary allocations) that developers can embed in code‑review templates.
• Education & onboarding: New hires can be taught the common energy‑aware patterns identified in the study, accelerating adoption of green coding practices without deep expertise in low‑level optimization.

Limitations & Future Work

• Dataset bias: The study relies on publicly visible PRs; private repositories or internal enterprise workflows may exhibit different energy‑awareness patterns.
• Keyword‑driven extraction: PRs that address energy implicitly (e.g., “reduce latency”) could have been missed, underestimating true agent awareness.
• Maintainability assessment: The paper infers maintainability concerns from reviewer comments but does not perform a systematic code‑quality analysis.
• Future directions: Extending the analysis to other AI agents, exploring automated metrics for readability vs. energy trade‑offs, and building a feedback loop where agents learn from rejected energy‑focused PRs.

Authors

• Tanjum Motin Mitul
• Md. Masud Mazumder
• Md Nahidul Islam Opu
• Shaiful Chowdhury

Paper Information

• arXiv ID: 2512.24636v1
• Categories: cs.SE
• Published: December 31, 2025
• PDF: Download PDF