🧠 NLP Model Testing Framework (Python | Pytest | AI/ML QA)

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🚀 Overview

This project is an NLP Model Testing Framework designed to validate machine learning model behavior using Python and Pytest.

Unlike traditional QA, this framework focuses on validating model outputs, ensuring predictions are:

• ✅ Accurate
• 🔁 Consistent
• ⚠️ Robust across edge cases
• 🧠 Reliable for real-world usage

It demonstrates how software testing evolves when working with AI/ML systems, where validating predictions is more critical than just testing functionality.

⚠️ Note: This project uses a rule-based simulation to demonstrate AI testing concepts. It is designed to showcase how QA validation works for AI systems such as LLMs and NLP models.

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🔥 Key Features

• NLP Sentiment Model Testing
• Text Preprocessing Validation
• Data-driven Testing using CSV
• Edge Case Handling (empty, numeric, mixed input)
• Automated Test Execution using Pytest
• Reusable Validation Logic
• Scalable Test Design for ML systems

⚠️ AI Risks Covered

• Hallucination
• Incorrect predictions
• Context loss
• Prompt variation inconsistency
• Unsafe responses

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🛠 Tech Stack

• Language: Python
• Testing Framework: Pytest
• Testing Approach: Data-driven Testing
• Domain: NLP (Sentiment Analysis)
• Concepts: AI/ML Testing, Model Validation

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📁 Project Structure

Project structure here

NLP-Model-Testing-Framework-Python-Pytest/

│
├── nlp_model/

    │ ├── __init__.py
    
    │ ├── sentiment_model.py
    
    │ └── text_preprocessor.py
│
├── tests/

    │ ├── __init__.py
    
    │ ├── test_sentiment_prediction.py
    
    │ ├── test_text_preprocessing.py
    
    │ ├── test_edge_cases.py
    
    │ └── test_data_driven_sentiment.py
│
├── test_data/

    │ └── sentiment_test_data.csv
│
├── screenshots/

    │ └── pytest-result.png
│
├── requirements.txt

└── README.md


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## 🧪 Test Scenarios Covered

### 1️⃣ Sentiment Prediction Testing

```python
def test_positive_sentiment():
    assert predict_sentiment("I love this product") == "positive"

2️⃣ Text Preprocessing Validation
def test_text_cleaning():
    assert clean_text("HELLO!!!") == "hello"

3️⃣ Edge Case Testing
Empty input
Numeric input
Mixed-case input

4️⃣ Data-Driven Testing (CSV-Based)
text,expected_sentiment
I love this,positive
This is bad,negative

▶️ How to Run
pip install -r requirements.txt
python -m pytest -v

📊 Test Execution Output (Pytest)
7 passed in 0.06s

📸 Test Execution Screenshot

🌍 Real-World Relevance

In AI/ML systems, testing is not limited to functionality.

We need to validate:

Model prediction correctness

Input variability handling

Data-driven validation

Edge case robustness

This framework simulates real-world ML validation scenarios, bridging the gap between traditional QA and AI testing.

🎯 What This Project Demonstrates

NLP Model Testing Approach

AI/ML Validation Techniques

Data-driven QA Strategy

Edge Case Handling in AI Systems

Python + Pytest Automation Skills

🚀 Future Enhancements

Integration with real ML models (Scikit-learn / HuggingFace)

Model accuracy metrics validation

Confusion matrix validation

CI/CD integration using GitHub Actions

Prompt-based NLP testing

👩‍💻 Author

Pragya Kapil

QA Automation | AI Testing | GenAI | NLP Testing