Test Metrics AI Dashboard

Predictive Analytics for QA Testing Optimization

Author: Neha Pathak

1. Project Overview

This project addresses a critical challenge in modern software testing: efficiently analyzing and predicting test failures in large-scale QA operations. Traditional testing approaches often struggle with:

• Identifying testing bottlenecks proactively
• Predicting potential failure areas
• Optimizing resource allocation for testing
• Visualizing testing metrics effectively

2. Problem Statement

Quality Assurance teams face several challenges:

• Time Consumption: Manual analysis of test metrics is time-consuming
• Resource Allocation: Difficulty in prioritizing testing efforts
• Pattern Recognition: Complex to identify patterns in test failures
• Predictive Capability: Lack of proactive failure prediction
• Visualization: Challenge in representing test metrics meaningfully

3. Solution Approach

This dashboard implements a machine learning-based solution that:

1. Analyzes Historical Data:

   • Test execution times
   • Resource utilization
   • Failure patterns
   • Performance metrics

2. Predicts Testing Outcomes:

   • Uses Random Forest algorithm
   • Identifies high-risk test areas
   • Predicts potential failures
   • Suggests optimization areas

3. Provides Visual Insights:

   • Interactive dashboards
   • Trend analysis
   • Performance metrics
   • Resource utilization patterns

4. Key Features

4.1 Data Processing

• Test execution metrics analysis
• Performance data collection
• Resource utilization tracking

4.2 Machine Learning Implementation

• Predictive modeling for test failures
• Pattern recognition in test data
• Feature importance analysis

4.3 Visualization

• Interactive dashboards
• Trend analysis plots
• Performance metrics visualization

5. Expected Outcomes

This project aims to:

• Reduce testing cycle time by 30%
• Improve test coverage efficiency
• Provide actionable insights for QA teams
• Enable data-driven testing decisions

6. Technologies Used

• Python: Primary programming language
• Pandas & NumPy: Data manipulation
• Scikit-learn: Machine learning implementation
• Plotly: Interactive visualizations

7. How to Use This Notebook

1. Run the cells sequentially
2. Follow the comments and documentation
3. Modify parameters as needed
4. Analyze the generated insights