🤖 Robotics BDD Framework

🧠 Overview

A Behavior-Driven Development (BDD) testing framework that integrates Robot Framework, Allure Reporting, and Kubernetes (K8s) orchestration for scalable, production-grade CI/CD automation.

This repository enables automation teams to:

Write human-readable test cases with Gherkin syntax.
Run tests locally or in distributed cloud-native environments.
Automatically generate and host interactive Allure reports.
Integrate with Docker and Kubernetes Jobs for high scalability.

🛠️ End-to-End DevOps & Kubernetes Workflow

This project is built on a comprehensive CI/CD pipeline and an automated Kubernetes deployment workflow:

1. Development & Testing (Local/CI)

Testing: Developers run benchmarks locally using Pytest with specific markers (-m gpu, -m cpu) to validate performance and collect detailed results.
CI/CD (GitHub Actions / Jenkins): The ci.yml workflow in GitHub Actions (or an equivalent Jenkins pipeline) is triggered upon code changes.
- It executes the benchmark tests against various hardware configurations.
- It uses Docker to ensure a consistent, reproducible environment for testing.
- It generates Allure Reports and plots system metrics (scripts/plot_gpu_metrics.py).

2. Packaging & Publishing

Docker Image Creation: Using one of the provided Dockerfile variants (Dockerfile.mini, Dockerfile.report), a Docker image containing the test environment, report server, and dependencies is built.
Registry Push: The final image is tagged and pushed to Docker Hub (or a private registry).

3. Automated Kubernetes Deployment

The deploy_gpu_workflow.py script manages the final deployment to a Kubernetes cluster:

Cluster Cleanup: It first runs kubectl delete deployment --all for a clean state.
Dynamic GPU Detection: It scans cluster nodes for available extended GPU resources (e.g., gpu.intel.com/i915, nvidia.com/gpu).
Resource Allocation: The deployment manifest is dynamically configured to request the detected GPU resource or fall back to standard CPU limits (1 core / 1Gi).
Deployment & Access: It creates the optimized Kubernetes Deployment and Service. Once the Pod is running, it initiates a blocking kubectl port-forward to map the cluster service (Port 80) to your local machine (Port 8080), allowing instant, interactive access to the Allure Report dashboard via http://127.0.0.1:8080.

🧩 Key Features

Robot Framework + Allure for structured, visual, and traceable test reporting.
Cross-platform CI/CD: Works on Windows (ci.bat) and Linux (ci.sh) agents.
Dockerized Test Execution ensures consistency and easy environment setup.
Kubernetes Integration to offload workloads to production-like clusters.
GitHub Actions / Jenkins support for CI/CD pipelines.

🏷️ Test Tags & Execution

Tag	Focus Area	Description
`navigation`	Path Planning	Safe movement, obstacle avoidance, waypoint following.
`pick_and_place`	Manipulation	Object handling, kinematics, and dynamic interactions.
`safety`	System Integrity	Collision prevention, boundary constraints, error handling.
`walking`	Gait Control	Posture, speed, stability, locomotion transitions.
`sensors`	Data Fusion	Sensor accuracy and Kalman Filter convergence.
`security`	System Security	System security checks.

⚙️ Setup Instructions

1. Local Environment Setup

git clone https://github.com/luckyjoy/robotics_bdd.git
cd robotics_bdd
pip install -r requirements.txt
pytest --alluredir=allure-results
allure serve allure-results

2. Run Tests Locally (Without Docker)

Mode	Command
Run All Tests	`pytest --verbose`
Run by Tag	`pytest -m sensors --verbose`
Sequential (OR)	`pytest -m "navigation or pick_and_place"`
Parallel	`pytest -m "navigation or safety" -n auto`

3. Run with Docker

docker build -t robotics-bdd:latest .
docker run --rm -v $(pwd)/reports:/reports robotics-bdd:latest

or python run_docker.py <build_number> [test_suite]

4. Run Advanced CI/CD with Kubernetes & Docker

Usage:   python run_kubernestes.py <Build_Number> [Suite_marker] [Dockerfile]
Example: python run_kubernestes.py 1 -m navigation Dockerfile.custom
-> Builds Docker images with , runs Robotics BDD Framework with  with build number 1, 
-> Generates Allure report, and pushes Docker images to Docker Hub.

Usage:   python deploy_gpu_workflow.py <Build_Number>
Example: python deploy_gpu_workflow.py 1
-> Creates the necessary Pod deployment and service, monitors Pod creation & reports scheduling events.
-> Deploys Docker image (build tag number 1) from Docker Hub, assigns a worker to run the Docker image within the assigned Pod.
-> Generates Allure Report.

📊 Allure Reporting

To generate and serve the Allure Report locally:

pytest --alluredir=allure-results
allure generate allure-results --clean -o allure-report
allure open allure-report

📸 Preview:

Opens an interactive HTML dashboard locally with detailed execution insights.

The CI pipeline will automatically attach and publish the generated reports as artifacts.

🧱 Project Structure

robotics_bdd/
├─ BUILD_NUMBER.txt           # File storing current build number.
├─ Dockerfile                 # Defines the BDD Test Docker image (runtime environment).
├─ Dockerfile.mini            # Minimal Docker build file
├─ Dockerfile.report          # Docker build file for the report server
├─ Jenkinsfile                # CI/CD pipeline definition for Jenkins.
├─ README.md
├─ requirements.txt			  # dependencies requirements
├─ run_docker.py			  # python script to run tests insie docker container.
├─ run_kubenestes.py          # Python script to orchestrate K8s jobs and report generation.
├─ kubenestes_pipeline.bat    # CI execution script.
├─ configs/                   # Kubernetes job configurations
├─ .github/                   # GitHub Actions CI/CD workflows
├─ features/                  # Gherkin feature files
│  └─ manual_tests/
├─ steps/                     # Python step definitions (pick_and_place_steps.py, navigation_steps.py, etc.)
├─ simulation/                # Robot simulation and core logic (robot_sim.py, sensors.py)
├─ reports/         	      # Static HTML docs (Test plan, PRD summary, metrics)
├─ allure-report/             # Dynamic history report files
└─ allure-results/            # Raw JSON/XML results (generated during test execution)

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## 🧩 Contributing

1. Fork the repository
2. Create a new branch (`feature/awesome-enhancement`)
3. Commit your changes
4. Open a Pull Request

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## 🪪 License

Released under the **MIT License** — free to use, modify, and distribute.

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📬 *Contact:* Bang Thien Nguyen [ontario1998@gmail.com](mailto:ontario1998@gmail.com) 

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> _“Build robots that test themselves before they move. That’s a true autonomy.”_

luckyjoy / robotics_bdd