📊 Non-Functional Testing & Performance Engineering Framework

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🚀 Overview

A specialized Non-Functional Testing (NFT) Framework engineered to audit backend performance, load resilience, and service contract integration.

Utilizing the GitHub REST API and enterprise endpoints as testing targets, this repository compiles structured automation suites via JMeter, SoapUI, and Bruno. It focuses on benchmarking transaction latencies, stress limits, and service layer reliability under concurrent thread distribution.

🎯 Focus: Performance benchmarking, load testing, SOAP/REST service validation, and load-resilience infrastructure engineering.

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🎯 Key Technical Features

• ⏱️ Performance & Load Simulation: Concurrency load profiles configured via Apache JMeter to pinpoint database degradation and application bottlenecks.
• 🔌 Enterprise SOAP & REST Validation: Comprehensive integration testing suites designed within SoapUI and Bruno to validate XML/JSON schemas and endpoints.
• 📋 Structured Test Planning (NFT): A formal architectural Test Plan documenting boundary parameters, high-availability thresholds, and service-level objectives (SLOs).
• 🔑 Dynamic Session Management: Automated runtime handling of environment attributes and API variables to ensure decoupled and clean suite execution.

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🏗️ Project Architecture Layout

The testing artifacts are organized into isolated modules tailored for automated runner engines:

Modulo02/
│
├── GitHub Api/       # Automated REST verification collections and configurations (Bruno/JMeter)
├── SOAP UI/          # XML Schema validation suites and SOAP endpoint testing packages
├── Test Plan/        # Non-functional requirements (NFRs), boundaries, and strategy manifest
└── README.md         # Core technical engineering documentation

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📊 Impact (CV-Level Highlights)

• 📈 Identified Latency Regressions: Designed load execution threads to simulate multi-user traffic, mapping structural server response degradation.
• 🛡️ Enforced Contract Integrity: Automated XML/JSON integration test checkpoints, ensuring stable interface contracts between internal backend layers.
• ⚙️ Mitigated Concurrency Issues: Formulated precise assertions to isolate system errors, transaction timeouts, and server execution failures under load.
• 📉 Optimized Testing Overhead: Replaced manual endpoint verifications with reusable collection templates, slashing technical test deployment lifecycles.

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⚙️ Execution & Setup Guide

1. Prerequisites

Ensure your local testing workstation has the following open-source binaries installed:

• Apache JMeter (v5.5 or higher)
• SmartBear SoapUI Open-Source
• Bruno CLI / Desktop App

2. Fetch the Infrastructure Repository

git clone https://github.com
cd Modulo02

3. Running Performance Tests (JMeter headless mode)

To execute the stress suites without GUI resource overhead, run:

jmeter -n -t "GitHub Api/your_test_plan.jmx" -l "GitHub Api/results.jtl"

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🔐 Performance Engineering Best Practices Applied

• Decoupled Key Management: Dynamic test variables pass parameters via decoupled config files, keeping private auth tokens fully abstracted.
• Headless Load Execution: Scripts are structured for execution outside of heavy GUIs to guarantee clean local thread-pool benchmarking.
• Granular Boundary Assertions: Requests deploy strict response-time limits and validation parameters to accurately capture infrastructure faults.

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👨‍💻 Author

Victor Guzmán
Computational Scientist | Backend Engineer | Software Quality & Test Automation Specialist

• 🔗 LinkedIn: https://linkedin.com