🛡️ QA COMMAND CENTER

Next-Gen Orchestration & Engineering Intelligence

A high-performance, ultra-premium observation layer bridging technical execution and executive decision-making.

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🌟 Executive Overview

This is more than a dashboard; it is a mission-critical ecosystem for modern engineering teams. Engineered with a Glassmorphism UI, it provides surgical-grade insights into your multi-project quality landscape, transforming chaotic logs into actionable intelligence.

Important

Unified Intelligence: Aggregated reporting for API (Behave) and GUI (Selenium) test suites in a single, high-fidelity pane.

🚀 The Four Pillars

• Unified Vision: Aggregated reporting for API (Behave) and GUI (Selenium) test suites in a single pane.
• Smart Diagnostics: Automated failure pattern recognition with immersive visual evidence.
• Performance Digital Twin: Real-time signal analysis and regression detection using Recharts analytics.
• Enterprise-Grade CI: Fully automated lifecycle from linting to report archival in AWS S3.

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📑 Table of Contents

• Executive Overview
• Live Experience
• Features Matrix
• Cutting-Edge Features
  • Performance Digital Twin
  • PR Intelligence
• Architecture
• CI/CD Ecosystem
• Navigation & Initialization
• Verification Engine
• Ecosystem Architecture
• Collaboration & Support

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🌐 Live Experience

The future of test reporting is already online. Experience the ultra-premium interface here:

🚀 Access Live Dashboard

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📊 Features Matrix

Ecosystem	Intelligence Layer	Technology Stack
Unified Reporting	Aggregated Vision for API & GUI	Behave, Selenium, Vite
Performance Digital Twin	Signal Velocity & Latency Audit	Recharts, Custom Algorithms
Visual Evidence	Automated High-Res Evidence Capture	html2canvas, AWS S3
Intelligent Dossiers	Executive-Ready PDF Artifacts	jsPDF, Modular Rendering
Incremental Cloud	High-Performance S3 Synchronization	AWS SDK, Incremental Sync
PR Intelligence	Dynamic labeling, Risk analysis & Code Churn Debt Metrics	GitHub Actions
Jira Intelligence	Bi-directional Sync: Test Plan > Feature > Scenario tracking	Jira REST API v3, Python

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✨ Cutting-Edge Features

📊 Performance Digital Twin

Go beyond binary pass/fail results. Our Performance Analytics Engine establishes a technical baseline for your system's health.

• Signal Velocity: Interactive time-series charts visualizing throughput trends.
• Throughput Chronology: Surgical detection of regression spikes and latency drifts.
• Spectral Latency Audit: A heat-map of system responsiveness and SLA adherence.

🤖 PR Intelligence

Maximize developer focus with an automated pull request management ecosystem powered by modular Actions:

• pr-hygiene-validator: Enforces strict Conventional Commits and minimum description requirements for clean histories.
• pr-size-labeler: Surgical application of dynamic (S, M, L, XL) sizing labels based on delta LOC limits.
• pr-risk-analyzer: Zero-day detection of changes in critical core files (like package.json or Workflows) raising blatant high-risk identifiers and Cautions within PR descriptions.
• pr-churn-analyzer: Intelligent calculation of the "Test Tech Debt" tracking Logic additions vs Testing additions to prevent regressions.

🔗 Jira Intelligence Ecosystem

Achieve 100% traceability between your testing codebase and product backlog via the jira-auto-tagger Action.

• Automated Hierarchy Definition: Instantiates a direct 1:N architectural alignment: Test Plan ➡️ Feature (Task) ➡️ Scenario (Sub-task).
• Auto-Tagger: Gherkin files are scanned automatically; missing mappings mandate the CI to create the corresponding Feature Tasks and Sub-tasks via the Rest V3 API, injecting the @CC-XXX tags into the codebase dynamically.
• Rich Task Enrichment: Extracts .feature description summaries, Gherkin Background steps, and Scenario matrices to populate the Jira task body cleanly.
• Roll-up Verification State: Execution status transitions flow recursively: Sub-task states flow up and enforce the parent Feature Task's final validation standard.

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🏗️ Architecture: Decoupled Full-Stack

We leverage a modern, decoupled architecture designed for scale and zero-downtime reliability.

graph TD
    subgraph "Verification Tier (Python)"
        A["GitHub Actions CI"] -->|Orchestrates| B("Behave API Logic")
        A -->|Orchestrates| C("Selenium GUI Logic")
        A -->|Orchestrates| L["Locust Performance Logic"]
    end

    subgraph "Intelligence Tier (Actions)"
        D["QA Hub Shared Actions"] -->|Standardizes| A
        D -->|Auto-Tags & Syncs Plan/Task/Subtask| J["Jira API (Cloud)"]
        J -.->|Updates| A
    end

    subgraph "Persistence Tier (Cloud)"
        B & C & L -->|Artifacts| E["JUnit XML / JSON"]
        E -->|Synchronized| S3["AWS S3 History"]
        E -->|Materialized| DB["SQLite Intelligence DB"]
        E -->|Transition States| J
    end

    subgraph "Presentation Tier (JS)"
        DB -->|Served via| G["Express Backend API"]
        G -->|Consumed by| H["Vite React Dashboard"]
        H -->|Hosted on| P["GitHub Pages (Global CDN)"]
    end
    
    style G fill:#6d28d9,color:#fff
    style P fill:#2563eb,color:#fff
    style D fill:#f59e0b,color:#000
    style H fill:#14b8a6,color:#fff
    style J fill:#0052CC,color:#fff

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Tip

The architecture strictly separates Execution, Persistence, and Presentation, enabling independent scaling and surgical updates without affecting system availability.

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🛠️ Performance-Driven CI/CD

Our pipelines are orchestrated via the QA Hub Actions library, ensuring modularity and global standards.

Status	Pipeline	Operational Responsibility
🧹	Lint Intelligence	Super-Linter enforcement for zero-debt documentation and logic.
🛡️	Unified Suite	Surgical execution of API, GUI, and Performance layers.
☁️	Incremental Sync	High-performance report archival with surgical delta detection.
🚀	SPA Deployment	Automated production deployment with native routing support.
🏷️	PR Orchestration	Dynamic labeling, intelligent summarization, and task tracking.

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🚦 Navigation & Initialization

📋 Prerequisites

Prepare your engineering environment with the following dependencies:

• Node.js: v20+ (LTS Preferred)
• Python: v3.11+
• Chrome / Chromedriver: Required for full GUI verification.
• AWS Infrastructure: Optional for remote persistent reporting.

💻 Full-Stack Setup

1. Registry Acquisition:

   git clone https://github.com/carlos-camara/dashboard.git
   cd dashboard

2. Ecosystem Initialization:

   # Presentation & Service Tier Dependencies
   npm install
   
   # Verification Tier (Python BDD)
   python -m venv .venv
   .venv\Scripts\activate  # Windows
   pip install -r requirements.txt

3. Service Manifestation:

   # Terminal A: Intelligence Backend (Express/SQLite)
   npm run start-backend
   
   # Terminal B: Presentation Layer (Vite/React)
   npm run dev

Important

The dashboard operates as a Decoupled SPA. Ensure both the backend (Port 3000) and frontend (Port 5173) are active for full interactivity.

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🧪 Verification Engine

This project includes a high-fidelity verification layer for API, GUI, and performance validation.

Registry Execution

# Activate Python virtual environment
.venv\Scripts\activate  # Windows

# API Integrity Suite
behave features/dashboard/api --tags=@smoke

# GUI Presentation Suite
behave features/dashboard/gui --tags=@smoke

# Performance Baseline Suite
behave features/dashboard/performance

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📁 Ecosystem Architecture

dashboard/
├── .github/workflows/          # CI/CD Orchestration
├── components/                 # Presentation Tier (Vite/React)
├── features/                   # Verification Tier (BDD/Python)
│   ├── dashboard/             # Project-Specific Scenarios
│   │   ├── api/               # API Verification
│   │   ├── gui/               # GUI Verification
│   │   └── performance/       # Performance Verification
│   ├── page_objects/          # Registry-Driven POM
│   ├── resources/             # Assets & Screenshots
│   └── steps/                 # Step Logic
├── reports/                    # Intelligence Artifacts
├── services/                   # Service Tier (Node.js/SQLite/S3)
├── server.js                   # Intelligence API Core
└── index.html                  # Presentation Core

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🤝 Collaboration & Support

We prioritize high-fidelity engineering standards and open collaboration.

• 📋 Contributing Guide
• 📝 Project Chronology
• 🛡️ Security Policy
• 📜 Code of Conduct

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Designed & Engineered with Precision by Carlos Cámara