Replication Package --- A Surrogate-based Approach for Faster 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.

The project is deliberately anonymous: no personal identifiers are included.

## 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:

```zshpython 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:

```zshpython 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.