[AI] Quality Assurance Testing

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Ticket Information

• Assigned Team: Product Team (with Engineering Team support)
• Dependencies: [AI] Search API Development #1809 (Search API Development)

Context & Background

Conduct comprehensive quality assurance testing and user acceptance testing for the AI article indexing system. This includes validation of recommendation quality, performance testing, user experience evaluation, and gathering feedback from legal team members.

Reference Documents:

• Phase 1 Implementation Plan: docs/ai/phase1-implementation.rst
• Success Criteria: nDCG@10 ≥ 0.8, response time < 2 seconds, 80% user satisfaction

Requirements & Acceptance Criteria

• Create comprehensive test cases with known relevant articles
• Prepare evaluation dataset with case descriptions and expected articles
• Validate case-to-article similarity accuracy and relevance
• Test performance with response time < 2 seconds requirement
• Conduct user acceptance testing with legal team members
• Gather feedback on recommendation relevance and usability
• Test system integration in case management workflow
• Document improvement suggestions and create iteration plan
• Validate overall system meets success criteria (nDCG@10 ≥ 0.8)

Implementation Steps

1. Test Data Preparation

Create comprehensive test dataset with the following components:

• Test Cases: 20+ legal case descriptions covering different practice areas
• Expected Results: For each test case, identify 3-5 most relevant articles
• Relevance Scores: Assign relevance scores (0.0-1.0) to expected articles
• Category Coverage: Ensure test cases cover all major legal categories
• Difficulty Levels: Include both simple and complex legal scenarios

Test case structure:

• Case ID and description
• Expected articles with relevance scores
• Legal categories and practice areas
• Difficulty level (simple, medium, complex)
• Special requirements or edge cases

2. Automated Quality Testing

Create automated test suite for quality validation:

• nDCG@10 Calculation: Implement Normalized Discounted Cumulative Gain metric
• Accuracy Testing: Validate recommendation accuracy across all test cases
• Performance Testing: Measure API response times for all test queries
• Consistency Testing: Ensure consistent results across multiple runs
• Edge Case Testing: Test system behavior with unusual or complex queries

Key test methods:

• test_recommendation_accuracy() - Validate nDCG@10 ≥ 0.8 requirement
• test_response_time_performance() - Validate response time < 2 seconds
• test_relevance_thresholds() - Test minimum similarity thresholds
• calculate_ndcg_at_k() - Calculate nDCG metrics for evaluation

3. User Acceptance Testing Framework

Design UAT framework for legal team testing:

• UAT Sessions: Structured testing sessions with legal team members
• Test Scenarios: Real-world case scenarios from legal practice
• Feedback Collection: Systematic collection of user feedback and ratings
• Usability Testing: Evaluate user interface and interaction patterns
• Workflow Integration: Test integration with existing case management processes

UAT components:

• User session management and tracking
• Feedback collection forms and ratings
• Performance monitoring during user sessions
• Documentation of user interactions and preferences

4. Performance Validation

Comprehensive performance testing:

• Response Time Testing: Validate < 2 second response time requirement
• Load Testing: Test system performance under multiple concurrent users
• Scalability Testing: Test system behavior with increasing data volume
• Resource Usage: Monitor CPU, memory, and database performance
• API Endpoint Testing: Test all API endpoints under various loads

Performance metrics:

• Average response time across all test cases
• 95th percentile response times
• System resource utilization
• Database query performance
• API throughput and concurrent user capacity

5. Quality Metrics Evaluation

Implement comprehensive quality evaluation:

• nDCG@10 Measurement: Calculate and validate nDCG scores
• Precision and Recall: Measure recommendation precision and recall
• User Satisfaction Scoring: Collect and analyze user satisfaction ratings
• Relevance Assessment: Evaluate relevance of recommended articles
• Category-Specific Performance: Analyze performance by legal category

Quality assessment framework:

• Automated metric calculation
• Statistical significance testing
• Performance comparison against baselines
• Category-wise performance analysis

6. User Feedback Collection and Analysis

Systematic feedback collection process:

• Structured Feedback Forms: Standardized forms for consistent feedback
• Rating Systems: 1-5 scale ratings for relevance and usefulness
• Qualitative Feedback: Open-ended feedback on user experience
• Improvement Suggestions: Collect specific improvement recommendations
• Usage Patterns: Analyze how users interact with the system

Feedback analysis:

• Quantitative analysis of ratings and scores
• Qualitative analysis of comments and suggestions
• Identification of common issues and improvement areas
• Prioritization of feedback for implementation

7. Integration Testing

Test system integration in real workflows:

• Case Management Integration: Test integration with existing case management
• Workflow Testing: Validate system fits into legal team workflows
• Data Consistency: Ensure consistent data across integrated systems
• User Authentication: Test authentication and authorization integration
• Performance in Production: Test system performance in production environment

Code Changes Required

• Create knowledge/tests/test_quality.py with automated quality tests
• Implement knowledge/tests/test_performance.py for performance validation
• Create UAT framework in knowledge/tests/uat_framework.py
• Add test data files with evaluation datasets
• Create feedback collection utilities
• Implement metrics calculation and reporting tools

External Documentation

• nDCG Metric Calculation
• Information Retrieval Evaluation
• User Acceptance Testing Best Practices
• Django Testing Documentation

Deliverables

1. Comprehensive test dataset with 20+ legal case scenarios
2. Automated quality testing suite with nDCG@10 validation
3. Performance testing results showing < 2 second response times
4. User acceptance testing framework and results
5. Detailed feedback analysis and user satisfaction metrics
6. Integration testing results and workflow validation
7. Quality assessment report with recommendations
8. Improvement roadmap based on testing results

Success Criteria Validation

• nDCG@10 ≥ 0.8: Automated testing validates recommendation quality
• Response Time < 2 seconds: Performance testing confirms speed requirement
• 99% Uptime: System stability testing over extended period
• 80% User Satisfaction: UAT results show 80%+ positive feedback

Testing Schedule

• Week 1: Test data preparation and automated testing setup
• Week 2: Automated quality and performance testing execution
• Week 3: User acceptance testing with legal team members
• Week 4: Feedback analysis and integration testing
• Week 5: Results compilation and improvement recommendations

Next Steps

• Upon completion, provide final assessment of Phase 1 implementation
• Create Phase 2 planning based on feedback and improvement suggestions
• Schedule production deployment planning with infrastructure team