AI Code Optimization for Sustainability: Dataset

AI Code Optimization for Sustainability: Dataset Refactoring Python Code for Energy-Efficiency using Qwen3: Dataset based on HumanEval, MBPP, and Mercury 📄 Read the Paper | HuggingFace Mirror | DOI: 10.5281/zenodo.18377893 | About the author This dataset is a part of a Master thesis research internship investigating the use of LLMs to optimize Python code for energy efficiency. The research was conducted as part of the Greenify My Code (GMC) project at the Netherlands Organisation for Applied Scientific Research (TNO). The code samples were curated from Mercury paper, dataset, Google MBPP (paper, dataset), and OpenAI HumanEval (paper, dataset) datasets. The 1,763 unique Python code samples analyzed before and after refactoring by Qwen3 models using four inference strategies (prompt and interaction style): cot-code-single, cot-code-plan, cot-suggestions-single, and cot-suggestions-plan. The analysis provides the following information (when possible): Functional correctness via provided test suites Static code analysis via Radon, eco-code-analyzer (custom fork used) and custom AST metrics Runtime profiling in a sandboxed environment using pyRAPL to measure energy consumption (µJ) under synthetic stress load (80% using stress-ng): baseline: 10ms (no code execution) warmup: 10ms (running test suite in a loop) profiling: 1s (running test suite in a loop) This analysis uses the following versions of Qwen3 models: 0.6B-Q8-GGUF 1.7B-Q8-GGUF 4B-Q8-GGUF 8B-Q8-GGUF 14B-Q8-GGUF 32B-Q8-GGUF Dataset Structure & Column Descriptions The dataset is structured hierarchically using dot-notation to separate nodes. The message column provides a status message of the entire process (success means that analysis before, refactoring, and analysis after were all successful) 1. Input Sample Metadata Information regarding the source code sample. Column Description input.sample_num Unique index identifier for the sample input.origin Source benchmark (Mercury, MBPP, or HumanEval) input.name Name of the function/task input.code Original Python source code input.test.initialization Test setup code to run before executing tests input.test.assertions List of assertions used for functional verification 2. Analysis These suffixes apply to both the original code (analysis_before) and the refactored code (analysis_after). Static Analysis Column Description analysis_*.static.radon.* Radon metrics analysis_*.static.ast.nodes Total nodes in the Abstract Syntax Tree analysis_*.static.ast.branching_factor total_branches divided by nodes_with_children analysis_*.static.ast.tree_depth Maximum depth of the AST analysis_*.static.eco.score Energy efficiency score (higher is better) analysis_*.static.eco.suggestions List of static analysis suggestions for energy improvement Runtime & Energy Profiling Measured using pyRAPL. Energy values are in Microjoules (µJ). Metrics starting with ... are the same for baseline, warmup, and profiling Column Description analysis_*.runtime.test.status Functional correctness status (passed, failed, error) ...duration_us Phase duration in microseconds ...util_avg_cpu Average CPU utilization ...util_avg_memory Average Memory utilization ...pkg_uj CPU Package Energy ...dram_uj DRAM Energy ...total_uj Total Energy (Package + DRAM) ...total_per_rep_uj Energy per single test execution 3. Greenify/Refactoring Process Details on the model, prompts, and the refactoring process. Columns starting with ... are the same for plan and refactor phases, however plan may be empty if a single-phase strategy is used. Column Description greenify.meta.input_type Context provided: just_code or code_with_suggestions greenify.meta.process_variation Strategy used: single_phase or plan_then_implement (two-step) greenify.meta.model.tag HuggingFace tag of the model ...prompt The prompt sent to the LLM for code generation ...tokens.* Token counts (input/output/total) for the given phase ...duration_seconds Time taken by the LLM to generate output ...rewritten_code LLM output: energy-optimized Python code ...explanation LLM output: self-written description for the reasoning of the applied changes Attribution and Licensing This dataset contains derivative work of the source datasets and is thus licensed under CC BY-NC 4.0.