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- Remove debugging service documentation (DEBUgging_SERVICES.md) - Remove development logs policy and quick reference guides - Remove E2E test creation summary - Remove gift certificate example file - Remove GitHub pull summary documentation
629 lines
20 KiB
Python
Executable File
629 lines
20 KiB
Python
Executable File
"""
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Reward System Integration Tests
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Comprehensive testing for agent rewards, incentives, and performance-based earnings
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"""
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import pytest
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import asyncio
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from datetime import datetime, timedelta
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from uuid import uuid4
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from typing import Dict, Any
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from sqlmodel import Session, select
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from sqlalchemy.exc import SQLAlchemyError
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from apps.coordinator_api.src.app.services.reward_service import (
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RewardEngine, RewardCalculator
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)
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from apps.coordinator_api.src.app.domain.rewards import (
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AgentRewardProfile, RewardTierConfig, RewardCalculation, RewardDistribution,
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RewardTier, RewardType, RewardStatus
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)
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from apps.coordinator_api.src.app.domain.reputation import AgentReputation, ReputationLevel
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|
|
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class TestRewardCalculator:
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"""Test reward calculation algorithms"""
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@pytest.fixture
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def calculator(self):
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return RewardCalculator()
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@pytest.fixture
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def sample_agent_reputation(self):
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return AgentReputation(
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agent_id="test_agent_001",
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trust_score=750.0,
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reputation_level=ReputationLevel.ADVANCED,
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performance_rating=4.5,
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reliability_score=85.0,
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community_rating=4.2,
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total_earnings=500.0,
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transaction_count=50,
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success_rate=92.0,
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jobs_completed=46,
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jobs_failed=4,
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average_response_time=1500.0,
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dispute_count=1,
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certifications=["advanced_ai", "expert_provider"],
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specialization_tags=["inference", "text_generation", "image_processing"],
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geographic_region="us-east"
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)
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def test_tier_multiplier_calculation(self, calculator, sample_agent_reputation):
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"""Test tier multiplier calculation based on trust score"""
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# Mock session behavior
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class MockSession:
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def exec(self, query):
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if hasattr(query, 'where'):
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return [sample_agent_reputation]
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return []
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session = MockSession()
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# Test different trust scores
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test_cases = [
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(950, 2.0), # Diamond
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(850, 1.5), # Platinum
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(750, 1.5), # Gold (should match config)
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(600, 1.2), # Silver
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(400, 1.1), # Silver
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(300, 1.0), # Bronze
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]
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for trust_score, expected_multiplier in test_cases:
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sample_agent_reputation.trust_score = trust_score
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multiplier = calculator.calculate_tier_multiplier(trust_score, session)
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assert 1.0 <= multiplier <= 2.0
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assert isinstance(multiplier, float)
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def test_performance_bonus_calculation(self, calculator):
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"""Test performance bonus calculation"""
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class MockSession:
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def exec(self, query):
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return []
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session = MockSession()
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# Test excellent performance
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excellent_metrics = {
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"performance_rating": 4.8,
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"average_response_time": 800,
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"success_rate": 96.0,
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"jobs_completed": 120
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}
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bonus = calculator.calculate_performance_bonus(excellent_metrics, session)
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assert bonus > 0.5 # Should get significant bonus
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# Test poor performance
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poor_metrics = {
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"performance_rating": 3.2,
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"average_response_time": 6000,
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"success_rate": 75.0,
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"jobs_completed": 10
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}
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bonus = calculator.calculate_performance_bonus(poor_metrics, session)
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assert bonus == 0.0 # Should get no bonus
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def test_loyalty_bonus_calculation(self, calculator):
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"""Test loyalty bonus calculation"""
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# Mock reward profile
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class MockSession:
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def exec(self, query):
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if hasattr(query, 'where'):
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return [AgentRewardProfile(
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agent_id="test_agent",
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current_streak=30,
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lifetime_earnings=1500.0,
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referral_count=15,
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community_contributions=25
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)]
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return []
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session = MockSession()
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bonus = calculator.calculate_loyalty_bonus("test_agent", session)
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assert bonus > 0.5 # Should get significant loyalty bonus
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# Test new agent
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class MockSessionNew:
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def exec(self, query):
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if hasattr(query, 'where'):
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return [AgentRewardProfile(
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agent_id="new_agent",
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current_streak=0,
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lifetime_earnings=10.0,
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referral_count=0,
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community_contributions=0
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)]
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return []
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session_new = MockSessionNew()
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bonus_new = calculator.calculate_loyalty_bonus("new_agent", session_new)
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assert bonus_new == 0.0 # Should get no loyalty bonus
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def test_referral_bonus_calculation(self, calculator):
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"""Test referral bonus calculation"""
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# Test high-quality referrals
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referral_data = {
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"referral_count": 10,
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"referral_quality": 0.9
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}
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bonus = calculator.calculate_referral_bonus(referral_data)
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expected_bonus = 0.05 * 10 * (0.5 + (0.9 * 0.5))
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assert abs(bonus - expected_bonus) < 0.001
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# Test no referrals
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no_referral_data = {
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"referral_count": 0,
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"referral_quality": 0.0
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}
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bonus = calculator.calculate_referral_bonus(no_referral_data)
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assert bonus == 0.0
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def test_total_reward_calculation(self, calculator, sample_agent_reputation):
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"""Test comprehensive reward calculation"""
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class MockSession:
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def exec(self, query):
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if hasattr(query, 'where'):
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return [sample_agent_reputation]
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return []
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session = MockSession()
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base_amount = 0.1 # 0.1 AITBC
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performance_metrics = {
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"performance_rating": 4.5,
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"average_response_time": 1500,
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"success_rate": 92.0,
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"jobs_completed": 50,
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"referral_data": {
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"referral_count": 5,
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"referral_quality": 0.8
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}
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}
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result = calculator.calculate_total_reward(
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"test_agent", base_amount, performance_metrics, session
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)
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# Verify calculation structure
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assert "base_amount" in result
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assert "tier_multiplier" in result
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assert "performance_bonus" in result
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assert "loyalty_bonus" in result
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assert "referral_bonus" in result
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assert "total_reward" in result
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assert "effective_multiplier" in result
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# Verify calculations
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assert result["base_amount"] == base_amount
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assert result["tier_multiplier"] >= 1.0
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assert result["total_reward"] >= base_amount
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assert result["effective_multiplier"] >= 1.0
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class TestRewardEngine:
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"""Test reward engine functionality"""
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@pytest.fixture
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def mock_session(self):
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"""Mock database session"""
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class MockSession:
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def __init__(self):
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self.data = {}
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self.committed = False
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def exec(self, query):
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# Mock query execution
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if hasattr(query, 'where'):
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return []
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return []
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def add(self, obj):
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self.data[obj.id if hasattr(obj, 'id') else 'temp'] = obj
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def commit(self):
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self.committed = True
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def refresh(self, obj):
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pass
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return MockSession()
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@pytest.fixture
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def reward_engine(self, mock_session):
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return RewardEngine(mock_session)
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def test_create_reward_profile(self, reward_engine, mock_session):
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"""Test creating a new reward profile"""
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agent_id = "test_agent_001"
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# Create profile
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profile = asyncio.run(
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reward_engine.create_reward_profile(agent_id)
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)
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# Verify profile creation
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assert profile.agent_id == agent_id
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assert profile.current_tier == RewardTier.BRONZE
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assert profile.tier_progress == 0.0
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assert mock_session.committed
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def test_calculate_and_distribute_reward(self, reward_engine, mock_session):
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"""Test reward calculation and distribution"""
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agent_id = "test_agent_001"
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reward_type = RewardType.PERFORMANCE_BONUS
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base_amount = 0.05
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performance_metrics = {
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"performance_rating": 4.5,
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"average_response_time": 1500,
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"success_rate": 92.0,
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"jobs_completed": 50
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}
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# Mock reputation
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mock_session.exec = lambda query: [AgentReputation(
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agent_id=agent_id,
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trust_score=750.0,
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reputation_level=ReputationLevel.ADVANCED
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)] if hasattr(query, 'where') else []
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# Calculate and distribute reward
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result = asyncio.run(
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reward_engine.calculate_and_distribute_reward(
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agent_id, reward_type, base_amount, performance_metrics
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)
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)
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# Verify result structure
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assert "calculation_id" in result
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assert "distribution_id" in result
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assert "reward_amount" in result
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assert "reward_type" in result
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assert "tier_multiplier" in result
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assert "status" in result
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# Verify reward amount
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assert result["reward_amount"] >= base_amount
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assert result["status"] == "distributed"
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def test_process_reward_distribution(self, reward_engine, mock_session):
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"""Test processing reward distribution"""
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# Create mock distribution
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distribution = RewardDistribution(
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id="dist_001",
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agent_id="test_agent",
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reward_amount=0.1,
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reward_type=RewardType.PERFORMANCE_BONUS,
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status=RewardStatus.PENDING
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)
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mock_session.exec = lambda query: [distribution] if hasattr(query, 'where') else []
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mock_session.add = lambda obj: None
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mock_session.commit = lambda: None
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mock_session.refresh = lambda obj: None
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# Process distribution
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result = asyncio.run(
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reward_engine.process_reward_distribution("dist_001")
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)
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# Verify processing
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assert result.status == RewardStatus.DISTRIBUTED
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assert result.transaction_id is not None
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assert result.transaction_hash is not None
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assert result.processed_at is not None
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assert result.confirmed_at is not None
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def test_update_agent_reward_profile(self, reward_engine, mock_session):
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"""Test updating agent reward profile"""
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agent_id = "test_agent_001"
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reward_calculation = {
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"base_amount": 0.05,
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"total_reward": 0.075,
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"performance_rating": 4.5
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}
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# Create mock profile
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profile = AgentRewardProfile(
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agent_id=agent_id,
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current_tier=RewardTier.BRONZE,
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base_earnings=0.1,
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bonus_earnings=0.02,
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total_earnings=0.12,
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lifetime_earnings=0.5,
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rewards_distributed=5,
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current_streak=3
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)
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mock_session.exec = lambda query: [profile] if hasattr(query, 'where') else []
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mock_session.commit = lambda: None
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# Update profile
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asyncio.run(
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reward_engine.update_agent_reward_profile(agent_id, reward_calculation)
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)
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# Verify updates
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assert profile.base_earnings == 0.15 # 0.1 + 0.05
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assert profile.bonus_earnings == 0.045 # 0.02 + 0.025
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assert profile.total_earnings == 0.195 # 0.12 + 0.075
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assert profile.lifetime_earnings == 0.575 # 0.5 + 0.075
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assert profile.rewards_distributed == 6
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assert profile.current_streak == 4
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assert profile.performance_score == 4.5
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def test_determine_reward_tier(self, reward_engine):
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"""Test reward tier determination"""
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test_cases = [
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(950, RewardTier.DIAMOND),
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(850, RewardTier.PLATINUM),
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(750, RewardTier.GOLD),
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(600, RewardTier.SILVER),
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(400, RewardTier.SILVER),
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(300, RewardTier.BRONZE),
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]
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for trust_score, expected_tier in test_cases:
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tier = reward_engine.determine_reward_tier(trust_score)
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assert tier == expected_tier
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def test_get_reward_summary(self, reward_engine, mock_session):
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"""Test getting reward summary"""
|
|
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|
agent_id = "test_agent_001"
|
|
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|
# Create mock profile
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profile = AgentRewardProfile(
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agent_id=agent_id,
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current_tier=RewardTier.GOLD,
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tier_progress=65.0,
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base_earnings=1.5,
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bonus_earnings=0.75,
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total_earnings=2.25,
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lifetime_earnings=5.0,
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rewards_distributed=25,
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current_streak=15,
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longest_streak=30,
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performance_score=4.2,
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last_reward_date=datetime.utcnow()
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)
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mock_session.exec = lambda query: [profile] if hasattr(query, 'where') else []
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|
# Get summary
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summary = asyncio.run(
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reward_engine.get_reward_summary(agent_id)
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)
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# Verify summary structure
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assert "agent_id" in summary
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assert "current_tier" in summary
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assert "tier_progress" in summary
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|
assert "base_earnings" in summary
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assert "bonus_earnings" in summary
|
|
assert "total_earnings" in summary
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|
assert "lifetime_earnings" in summary
|
|
assert "rewards_distributed" in summary
|
|
assert "current_streak" in summary
|
|
assert "longest_streak" in summary
|
|
assert "performance_score" in summary
|
|
assert "recent_calculations" in summary
|
|
assert "recent_distributions" in summary
|
|
|
|
def test_batch_process_pending_rewards(self, reward_engine, mock_session):
|
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"""Test batch processing of pending rewards"""
|
|
|
|
# Create mock pending distributions
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distributions = [
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RewardDistribution(
|
|
id=f"dist_{i}",
|
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agent_id="test_agent",
|
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reward_amount=0.1,
|
|
reward_type=RewardType.PERFORMANCE_BONUS,
|
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status=RewardStatus.PENDING,
|
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priority=5
|
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)
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for i in range(5)
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]
|
|
|
|
mock_session.exec = lambda query: distributions if hasattr(query, 'where') else []
|
|
mock_session.add = lambda obj: None
|
|
mock_session.commit = lambda: None
|
|
mock_session.refresh = lambda obj: None
|
|
|
|
# Process batch
|
|
result = asyncio.run(
|
|
reward_engine.batch_process_pending_rewards(limit=10)
|
|
)
|
|
|
|
# Verify batch processing
|
|
assert "processed" in result
|
|
assert "failed" in result
|
|
assert "total" in result
|
|
assert result["total"] == 5
|
|
assert result["processed"] + result["failed"] == result["total"]
|
|
|
|
def test_get_reward_analytics(self, reward_engine, mock_session):
|
|
"""Test getting reward analytics"""
|
|
|
|
# Create mock distributions
|
|
distributions = [
|
|
RewardDistribution(
|
|
id=f"dist_{i}",
|
|
agent_id=f"agent_{i}",
|
|
reward_amount=0.1 * (i + 1),
|
|
reward_type=RewardType.PERFORMANCE_BONUS,
|
|
status=RewardStatus.DISTRIBUTED,
|
|
created_at=datetime.utcnow() - timedelta(days=i)
|
|
)
|
|
for i in range(10)
|
|
]
|
|
|
|
mock_session.exec = lambda query: distributions if hasattr(query, 'where') else []
|
|
|
|
# Get analytics
|
|
analytics = asyncio.run(
|
|
reward_engine.get_reward_analytics(
|
|
period_type="daily",
|
|
start_date=datetime.utcnow() - timedelta(days=30),
|
|
end_date=datetime.utcnow()
|
|
)
|
|
)
|
|
|
|
# Verify analytics structure
|
|
assert "period_type" in analytics
|
|
assert "start_date" in analytics
|
|
assert "end_date" in analytics
|
|
assert "total_rewards_distributed" in analytics
|
|
assert "total_agents_rewarded" in analytics
|
|
assert "average_reward_per_agent" in analytics
|
|
assert "tier_distribution" in analytics
|
|
assert "total_distributions" in analytics
|
|
|
|
# Verify calculations
|
|
assert analytics["total_rewards_distributed"] > 0
|
|
assert analytics["total_agents_rewarded"] > 0
|
|
assert analytics["average_reward_per_agent"] > 0
|
|
|
|
|
|
class TestRewardIntegration:
|
|
"""Integration tests for reward system"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_full_reward_lifecycle(self):
|
|
"""Test complete reward lifecycle"""
|
|
|
|
# This would be a full integration test with actual database
|
|
# For now, we'll outline the test structure
|
|
|
|
# 1. Create agent profile
|
|
# 2. Create reputation profile
|
|
# 3. Calculate and distribute multiple rewards
|
|
# 4. Verify tier progression
|
|
# 5. Check analytics
|
|
# 6. Process batch rewards
|
|
|
|
pass
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_reward_tier_progression(self):
|
|
"""Test reward tier progression based on performance"""
|
|
|
|
# Test that agents progress through reward tiers
|
|
# as their trust scores and performance improve
|
|
|
|
pass
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_reward_calculation_consistency(self):
|
|
"""Test reward calculation consistency across different scenarios"""
|
|
|
|
# Test that reward calculations are consistent
|
|
# and predictable across various input scenarios
|
|
|
|
pass
|
|
|
|
|
|
# Performance Tests
|
|
class TestRewardPerformance:
|
|
"""Performance tests for reward system"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_bulk_reward_calculations(self):
|
|
"""Test performance of bulk reward calculations"""
|
|
|
|
# Test calculating rewards for many agents
|
|
# Should complete within acceptable time limits
|
|
|
|
pass
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_batch_distribution_performance(self):
|
|
"""Test batch reward distribution performance"""
|
|
|
|
# Test that batch reward distributions are fast
|
|
# Even with large numbers of pending rewards
|
|
|
|
pass
|
|
|
|
|
|
# Utility Functions
|
|
def create_test_reward_profile(agent_id: str, **kwargs) -> Dict[str, Any]:
|
|
"""Create test reward profile data for testing"""
|
|
|
|
defaults = {
|
|
"agent_id": agent_id,
|
|
"current_tier": RewardTier.BRONZE,
|
|
"tier_progress": 0.0,
|
|
"base_earnings": 0.0,
|
|
"bonus_earnings": 0.0,
|
|
"total_earnings": 0.0,
|
|
"lifetime_earnings": 0.0,
|
|
"rewards_distributed": 0,
|
|
"current_streak": 0,
|
|
"longest_streak": 0,
|
|
"performance_score": 0.0,
|
|
"loyalty_score": 0.0,
|
|
"referral_count": 0,
|
|
"community_contributions": 0
|
|
}
|
|
|
|
defaults.update(kwargs)
|
|
return defaults
|
|
|
|
|
|
def create_test_performance_metrics(**kwargs) -> Dict[str, Any]:
|
|
"""Create test performance metrics for testing"""
|
|
|
|
defaults = {
|
|
"performance_rating": 3.5,
|
|
"average_response_time": 3000.0,
|
|
"success_rate": 85.0,
|
|
"jobs_completed": 25,
|
|
"referral_data": {
|
|
"referral_count": 0,
|
|
"referral_quality": 0.5
|
|
}
|
|
}
|
|
|
|
defaults.update(kwargs)
|
|
return defaults
|
|
|
|
|
|
# Test Configuration
|
|
@pytest.fixture(scope="session")
|
|
def test_config():
|
|
"""Test configuration for reward system tests"""
|
|
|
|
return {
|
|
"test_agent_count": 100,
|
|
"test_reward_count": 500,
|
|
"test_distribution_count": 1000,
|
|
"performance_threshold_ms": 1000,
|
|
"memory_threshold_mb": 100
|
|
}
|
|
|
|
|
|
# Test Markers
|
|
pytest.mark.unit = pytest.mark.unit
|
|
pytest.mark.integration = pytest.mark.integration
|
|
pytest.mark.performance = pytest.mark.performance
|
|
pytest.mark.slow = pytest.mark.slow
|