time_modules

Time Modules

This directory contains scripts and decorators used to measure execution time during benchmark runs in our study: "Python Under the Microscope: A Comparative Energy Analysis of Execution Methods."

All time readings are collected using Python’s native time.time() method, offering lightweight, reproducible, and fine-grained runtime profiling with minimal overhead.

Directory Structure

time_modules/
├── measure_time_to_csv.py     # Benchmark decorator for time logging
└── README.md                  # This file

Module Description

Module	Description
measure_time_to_csv.py	Decorator-based utility to wrap benchmark functions and log their runtime
time.time()	Standard Python function used for wall-clock time measurement
psutil + platform	Libraries used to log system configuration during measurement

Features

• Measures runtime in seconds using time.time() with microsecond resolution.
• Executes the benchmark n times, logs each repetition’s duration in a structured CSV.
• Automatically logs system information (CPU, RAM, OS, architecture) into a JSON file.
• Output is saved per benchmark, and organized under a dedicated folder (time_benchmark/).
• Suitable for measuring short or long-running CPU-bound Python scripts.

Setup Instructions

This module uses only standard Python libraries except for psutil. Install it as follows:

pip install psutil

No root privileges or platform-specific configurations are required.

Example Usage

from measure_time_to_csv import measure_time_to_csv

@measure_time_to_csv(n=50, csv_filename="binary_tree", folder_name="time_benchmark")
def run_binary_tree():
    # Your benchmark logic here
    pass

run_binary_tree()

This will:

• Run the run_binary_tree() function 50 times
• Save results to time_benchmark/binary_tree.csv
• Save system metadata to time_benchmark/system_info.json

Output Format

CSV: Per-run execution log

timestamp	function	run	execution_time (s)
2025-07-09T08:32:01	run_binary_tree	1	0.01873
...	...	...	...

JSON: System information

{
  "CPU": "Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz",
  "RAM_GB": 15.85,
  "OS": "Linux 5.15.0-91-generic",
  "Architecture": "x86_64",
  "Test_Result_File": "time_benchmark/binary_tree.csv"
}

Repetition and Statistical Significance

In this study, each benchmark was executed 50 times to reduce noise and improve result stability. This helps account for background processes, thermal variations, and OS scheduling effects.

Notes on Accuracy

• time.time() captures wall-clock duration, which is suitable for coarse-grained benchmarking.
• Results were averaged across runs, then normalized per algorithm for GreenScore computation.
• While not cycle-accurate like hardware timers, this approach provides consistent, cross-platform results suitable for comparative analysis.