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

---

🚀 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.

---

🔥 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

⚠️ Validation Risks Covered

• Incorrect sentiment classification
• Prediction inconsistency against expected outputs
• Text preprocessing errors
• Empty-input handling
• Numeric-input handling
• Input-format variation
• Regression against golden test data

---

🛠 Tech Stack

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

---

📁 Project Structure

NLP-Model-Testing-Framework-Python-Pytest/
│
├── .github/
│   └── workflows/
│       └── ci.yml
│
├── 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

---

🧪 Test Scenarios Covered

Sentiment Prediction Validation

The test suite validates positive, negative and neutral sentiment behavior.

def test_positive_sentiment():
    result = predict_sentiment("I am very happy today")
    assert result == "positive"

Text Preprocessing

Input normalization is validated before model-style processing.

def test_text_cleaning():
    result = clean_text("Hello!!! How are you??")
    assert result == "hello how are you"

Edge Cases

Coverage includes:

• Empty input
• Numeric input
• Positive text
• Negative text
• Neutral text

Data-Driven Validation

Expected inputs and outputs are maintained in:

test_data/sentiment_test_data.csv

Example:

text,expected_sentiment
I love this product,positive
This is bad service,negative
I am walking in park,neutral

This acts as a small golden test dataset for regression validation.

---

▶️ Running the Tests

pip install -r requirements.txt
python -m pytest -v

Current execution:

7 passed

---

📸 Test Execution Evidence

🌍 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

Prompt-based NLP testing

👩‍💻 Author

Pragya Kapil

QA Automation | AI Testing | GenAI | NLP Testing