Replication Package: A Surrogate-based Approach for Fast Multi-objective Architectural Refactoring Optimization

Multi-Objective Evaluation Utilities This repository contains two Python scripts used to post-process multi-objective optimization experiments: quality_indicator.py computes Pareto-based quality indicators across multiple runs and generates comparison plots. resource_usage.py aggregates runtime and resource metrics from experiment logs and produces trend visualizations and summaries. Prerequisites Python 3.10+ (tested with Python 3.11) Dependencies listed in requirements.txt (install with pip install -r requirements.txt) Expected Data Layout Both scripts assume experiment outputs exist in sibling folders to the project root. By default the experiments are named: nsgaii-ccm-eval-102-surrogate-50 nsgaii-ccm-eval-102-surrogate-false Each experiment should contain multiple runs structured as: <experiment>/ run1/ experiment.json algo_perf_stats.json run2/ experiment.json algo_perf_stats.json ... Adjust the experiment names in the scripts if your folders differ. Usage Quality indicators quality_indicator.py merges Pareto fronts from all runs, computes metrics (HV, IGD+, GD+, epsilon) with pymoo and jMetalPy, and saves per-metric comparison plots. Run from the project root: python quality_indicator.py Outputs: PNG figures named like hv_quality_indicator_comparison.png in the current directory. Resource usage analysis resource_usage.py loads algo_perf_stats.json files, normalizes differing JSON shapes, and computes mean/std trends for detected numeric resource columns. It also derives execution time and memory summaries when available. Run from the project root: python resource_usage.py Outputs are written under results/resource_trends/, including per-resource plots, an overview grid, optional CSV summaries, and markdown/ASCII tables for execution times. Notes The scripts rely on Matplotlib and Seaborn; a non-headless environment or appropriate backend may be needed for figure generation No external credentials or user-specific configuration are required; paths are relative to the repository root.