# Test Specification Schema Designs and Algorithms

## Detailed Technical Reference Guide

This document provides PostgreSQL schemas and pseudocode algorithms for implementing the advanced test specification models from enterprise leaders.

---

## PART 1: DATABASE SCHEMA DESIGNS

### 1.1 Core Test Specification Schema

```sql
-- Test Specification Table
CREATE TABLE test_specifications (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    description TEXT,
    classification_category VARCHAR(50),
    classification_layer VARCHAR(50),
    priority VARCHAR(20),
    criticality_score FLOAT DEFAULT 0.5,

    -- Source information
    module VARCHAR(255),
    class_name VARCHAR(255),
    method_name VARCHAR(255),
    git_repository VARCHAR(500),
    git_branch VARCHAR(100),
    code_location_file VARCHAR(500),
    code_location_line_number INTEGER,

    -- Execution metadata
    timeout_seconds INTEGER DEFAULT 300,
    max_retry_attempts INTEGER DEFAULT 3,
    retry_backoff_strategy VARCHAR(50) DEFAULT 'exponential',
    retry_backoff_ms INTEGER DEFAULT 1000,

    -- Flakiness tracking
    historical_pass_rate FLOAT DEFAULT 1.0,
    flakiness_score FLOAT DEFAULT 0.0,

    -- Performance baseline
    p50_latency_ms FLOAT,
    p95_latency_ms FLOAT,
    p99_latency_ms FLOAT,
    max_duration_ms FLOAT,

    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW(),
    created_by VARCHAR(255),
    CONSTRAINT valid_criticality CHECK (criticality_score >= 0 AND criticality_score <= 1),
    CONSTRAINT valid_flakiness CHECK (flakiness_score >= 0 AND flakiness_score <= 1),
    CONSTRAINT valid_pass_rate CHECK (historical_pass_rate >= 0 AND historical_pass_rate <= 1)
);

-- Test Oracle Configuration
CREATE TABLE test_oracles (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),
    oracle_type VARCHAR(50) NOT NULL, -- Specification, Derived, Pseudo, Consistency, Statistical

    -- Specification oracle
    expected_output_schema JSONB,

    -- Pseudo oracle
    reference_implementation_url VARCHAR(500),
    reference_implementation_language VARCHAR(50),
    reference_implementation_version VARCHAR(50),

    -- Statistical oracle
    confidence_threshold FLOAT,
    uncertainty_range_min FLOAT,
    uncertainty_range_max FLOAT,

    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

-- Test Output Validators
CREATE TABLE test_output_validators (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_oracle_id UUID NOT NULL REFERENCES test_oracles(id),
    json_path VARCHAR(500),
    comparison_strategy VARCHAR(50), -- Exact, Fuzzy, Regex, Range, Custom
    expected_value TEXT,
    tolerance_percent FLOAT,

    created_at TIMESTAMP DEFAULT NOW()
);

-- Test Assertions
CREATE TABLE test_assertions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),
    assertion_type VARCHAR(50) NOT NULL, -- Value, Exception, Performance, State
    target_path VARCHAR(500),
    operator VARCHAR(50), -- Equals, Contains, Matches, GreaterThan, Custom
    expected_value TEXT,
    severity VARCHAR(20), -- Blocker, Critical, Major, Minor
    error_message TEXT,

    created_at TIMESTAMP DEFAULT NOW()
);

-- Test Parameters (for parameterized tests)
CREATE TABLE test_parameters (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),
    parameter_name VARCHAR(255),
    parameter_type VARCHAR(50), -- String, Integer, Boolean, Object, Array
    parameter_value JSONB,

    created_at TIMESTAMP DEFAULT NOW()
);

-- Flakiness Patterns
CREATE TABLE flakiness_patterns (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),
    condition_description TEXT,
    frequency_percentage FLOAT,
    affected_environments TEXT[],
    root_cause_id UUID,

    created_at TIMESTAMP DEFAULT NOW()
);

-- Test Dependencies
CREATE TABLE test_dependencies (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    blocking_test_id UUID NOT NULL REFERENCES test_specifications(id),
    dependent_test_id UUID NOT NULL REFERENCES test_specifications(id),
    dependency_type VARCHAR(50), -- Blocking, Integration, Shared_Resources

    created_at TIMESTAMP DEFAULT NOW(),
    CONSTRAINT no_self_dependency CHECK (blocking_test_id != dependent_test_id)
);

-- Test Coverage Mapping
CREATE TABLE test_coverage_mapping (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),
    file_path VARCHAR(500),
    line_numbers INTEGER[],
    branch_ids INTEGER[],

    created_at TIMESTAMP DEFAULT NOW()
);

-- Test Requirement Traceability
CREATE TABLE test_requirement_traceability (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),
    requirement_id VARCHAR(255),
    epic_id UUID,
    story_id UUID,

    created_at TIMESTAMP DEFAULT NOW()
);
```

### 1.2 Test Data Factory Schema

```sql
CREATE TABLE test_data_factories (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    entity_type VARCHAR(255),
    generation_approach VARCHAR(50), -- Deterministic, RandomSynthetic, Production_Subset, Combinatorial, HybridMix

    -- Synthetic generation
    model_type VARCHAR(50), -- Pattern_Based, Statistical, ML_Generative, Semantic
    seed_value INTEGER,

    -- Production masking
    masking_enabled BOOLEAN DEFAULT FALSE,

    -- Data relationships
    primary_key_field VARCHAR(255),

    -- Quality metrics
    uniqueness_required BOOLEAN,
    uniqueness_ratio FLOAT,
    null_percentage FLOAT,
    data_validity_percentage FLOAT,

    -- Refresh policy
    refresh_schedule VARCHAR(100), -- Cron expression
    retention_days INTEGER,
    versioning_enabled BOOLEAN,

    -- Performance
    generation_time_ms FLOAT,
    generation_throughput_records_sec INTEGER,
    storage_size_mb FLOAT,
    cache_hit_ratio FLOAT,

    -- Audit
    created_by VARCHAR(255),
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW(),
    last_used TIMESTAMP,
    usage_count INTEGER DEFAULT 0,
    compliance_status VARCHAR(50) -- Compliant, Review_Required, Non_Compliant
);

CREATE TABLE data_generation_constraints (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    factory_id UUID NOT NULL REFERENCES test_data_factories(id),
    field_name VARCHAR(255),
    constraint_type VARCHAR(50), -- Range, Regex, ForeignKey, Custom
    constraint_expression TEXT,
    constraint_priority VARCHAR(20), -- Hard, Soft

    created_at TIMESTAMP DEFAULT NOW()
);

CREATE TABLE data_masking_rules (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    factory_id UUID NOT NULL REFERENCES test_data_factories(id),
    field_pattern VARCHAR(500),
    masking_technique VARCHAR(50), -- Hash, Shuffle, Fake, Encryption, Pseudonymization
    replacement_format VARCHAR(255),

    created_at TIMESTAMP DEFAULT NOW()
);

CREATE TABLE data_relationship_constraints (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    factory_id UUID NOT NULL REFERENCES test_data_factories(id),
    foreign_key_field VARCHAR(255),
    target_entity VARCHAR(255),
    cardinality VARCHAR(20), -- 1-1, 1-N

    created_at TIMESTAMP DEFAULT NOW()
);

CREATE TABLE test_data_sets (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    factory_id UUID NOT NULL REFERENCES test_data_factories(id),
    name VARCHAR(255),
    source_type VARCHAR(50), -- Production_Subset, Synthetic, Seed_Based
    schema_definition JSONB,
    sample_size INTEGER,

    created_at TIMESTAMP DEFAULT NOW()
);
```

### 1.3 Test Execution and Analytics Schema

```sql
-- Test Execution Events
CREATE TABLE test_execution_events (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),
    execution_id UUID,

    -- Execution details
    start_time TIMESTAMP NOT NULL,
    end_time TIMESTAMP,
    duration_ms INTEGER,

    -- Result
    status VARCHAR(50), -- Passed, Failed, Skipped, Error, Timeout
    failure_message TEXT,
    stack_trace TEXT,

    -- Environment
    environment_name VARCHAR(255),
    test_data_set_id UUID,

    -- Flakiness indicator
    is_flaky BOOLEAN DEFAULT FALSE,
    retry_count INTEGER DEFAULT 0,

    -- Performance metrics
    wall_clock_time_ms FLOAT,
    cpu_time_ms FLOAT,
    memory_peak_mb FLOAT,

    created_at TIMESTAMP DEFAULT NOW(),
    CONSTRAINT valid_status CHECK (status IN ('Passed', 'Failed', 'Skipped', 'Error', 'Timeout'))
);

-- Flakiness Score History
CREATE TABLE flakiness_score_history (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),

    pass_rate_percentage FLOAT,
    flakiness_score FLOAT,
    failure_variance FLOAT,

    confidence_lower_bound_95pct FLOAT,
    confidence_upper_bound_95pct FLOAT,
    sample_size_executions INTEGER,

    -- Environmental correlations
    latency_sensitive BOOLEAN,
    concurrency_sensitive BOOLEAN,
    timing_sensitive BOOLEAN,
    resource_sensitive BOOLEAN,
    machine_specific BOOLEAN,
    network_dependent BOOLEAN,

    calculated_at TIMESTAMP NOT NULL,
    measurement_window_hours INTEGER
);

-- Test Impact Analysis Results
CREATE TABLE test_impact_analysis_results (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    commit_hash VARCHAR(100),

    -- Changed files in commit
    changed_files TEXT[],

    -- Tests selected for execution
    selected_test_count INTEGER,
    total_test_count INTEGER,
    reduction_percentage FLOAT,

    -- Impact reasoning
    code_impact_selected BOOLEAN,
    previously_failing_selected BOOLEAN,
    newly_added_selected BOOLEAN,

    -- Predicted outcomes
    regression_detection_probability FLOAT,

    analysis_timestamp TIMESTAMP DEFAULT NOW(),
    execution_status VARCHAR(50), -- Pending, Running, Complete, Failed
    actual_regressions_detected INTEGER
);

-- Test-to-Code Impact Mapping
CREATE TABLE test_code_impact_map (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    test_specification_id UUID NOT NULL REFERENCES test_specifications(id),
    source_file VARCHAR(500),
    line_numbers INTEGER[],
    branch_ids INTEGER[],
    impact_type VARCHAR(50), -- Direct, Indirect, Transitive
    impact_confidence FLOAT,

    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);
```

### 1.4 Requirements and Traceability Schema

```sql
-- SAFe Epics
CREATE TABLE epics (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(500) NOT NULL,
    description TEXT,
    strategic_theme VARCHAR(255),

    -- Epic scope
    epic_scope VARCHAR(50), -- Product_Enhancement, Platform_Capability, etc.
    timeframe VARCHAR(50), -- Single_Sprint, One_to_Two_Quarters, One_Year, Multi_Year

    -- Business value
    business_value_statement TEXT,
    kpi_baseline FLOAT,
    kpi_target FLOAT,

    -- Implementation
    target_release VARCHAR(100),

    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW(),
    created_by VARCHAR(255),
    status VARCHAR(50) -- Not_Started, In_Progress, Complete, Deferred
);

-- SAFe Capabilities
CREATE TABLE capabilities (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    epic_id UUID NOT NULL REFERENCES epics(id),
    name VARCHAR(500) NOT NULL,
    description TEXT,

    functional_area VARCHAR(255),
    stakeholders TEXT[],

    created_at TIMESTAMP DEFAULT NOW(),
    status VARCHAR(50)
);

-- SAFe Features
CREATE TABLE features (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    capability_id UUID NOT NULL REFERENCES capabilities(id),
    epic_id UUID NOT NULL REFERENCES epics(id),

    name VARCHAR(500) NOT NULL,
    description TEXT,
    use_case TEXT,

    created_at TIMESTAMP DEFAULT NOW(),
    status VARCHAR(50)
);

-- User Stories
CREATE TABLE user_stories (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    feature_id UUID NOT NULL REFERENCES features(id),
    capability_id UUID NOT NULL REFERENCES capabilities(id),
    epic_id UUID NOT NULL REFERENCES epics(id),

    user_story_statement TEXT NOT NULL, -- "As a [role] I want [action] so that [benefit]"
    description TEXT,

    story_type VARCHAR(50), -- New_Feature, Enhancement, Defect_Fix, Technical_Debt

    -- INVEST characteristics
    independent BOOLEAN DEFAULT TRUE,
    negotiable BOOLEAN DEFAULT TRUE,
    valuable_to_stakeholder BOOLEAN DEFAULT TRUE,
    estimable BOOLEAN DEFAULT TRUE,
    small BOOLEAN DEFAULT TRUE,
    testable BOOLEAN DEFAULT TRUE,

    -- Estimation
    story_points INTEGER,
    optimistic_points INTEGER,
    most_likely_points INTEGER,
    pessimistic_points INTEGER,
    estimated_hours FLOAT,

    created_at TIMESTAMP DEFAULT NOW(),
    status VARCHAR(50), -- Backlog, Ready_for_Development, In_Development, Testing, Done
    assigned_to VARCHAR(255),
    completed_date TIMESTAMP
);

-- Story Acceptance Criteria
CREATE TABLE story_acceptance_criteria (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    story_id UUID NOT NULL REFERENCES user_stories(id),

    criterion_description TEXT NOT NULL,
    verification_method VARCHAR(50), -- Manual_Test, Automated_Test, Code_Review, Demonstration
    is_satisfied BOOLEAN DEFAULT FALSE,

    created_at TIMESTAMP DEFAULT NOW()
);

-- Story Dependencies
CREATE TABLE story_dependencies (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    blocking_story_id UUID NOT NULL REFERENCES user_stories(id),
    dependent_story_id UUID NOT NULL REFERENCES user_stories(id),

    created_at TIMESTAMP DEFAULT NOW(),
    CONSTRAINT no_self_dep CHECK (blocking_story_id != dependent_story_id)
);

-- Requirement Traceability
CREATE TABLE requirement_traceability (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    requirement_id VARCHAR(255) PRIMARY KEY,
    epic_id UUID REFERENCES epics(id),
    capability_id UUID REFERENCES capabilities(id),
    feature_id UUID REFERENCES features(id),
    story_id UUID REFERENCES user_stories(id),

    test_coverage_ids UUID[],
    code_files VARCHAR(500)[],

    traceability_complete BOOLEAN DEFAULT FALSE,
    created_at TIMESTAMP DEFAULT NOW()
);
```

### 1.5 Defect Management Schema

```sql
CREATE TABLE defects (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    title VARCHAR(500) NOT NULL,
    description TEXT,

    -- Classification
    defect_type VARCHAR(50), -- Functionality_Missing, UI_Usability, Performance_Degradation, etc.
    severity VARCHAR(20), -- Critical, Major, Medium, Minor, Trivial
    priority VARCHAR(20), -- P0_Blocker, P1_Critical, P2_High, P3_Medium, P4_Low

    -- Origin and phase
    origin_phase VARCHAR(50), -- Requirements, Design, Implementation, Testing, Environment
    phase_introduced VARCHAR(50),
    phase_detected VARCHAR(50),

    -- Impact
    affected_modules TEXT[],
    affected_features TEXT[],
    customer_visible BOOLEAN DEFAULT FALSE,
    regulatory_impact BOOLEAN DEFAULT FALSE,
    security_impact BOOLEAN DEFAULT FALSE,

    -- Reproduction
    reproducibility VARCHAR(50), -- Always, Often, Intermittent, Rarely, Unable_to_Reproduce
    steps_to_reproduce TEXT,
    expected_behavior TEXT,
    actual_behavior TEXT,

    -- Workflow
    status VARCHAR(50), -- New, Triaged, Assigned, In_Progress, Fixed, Testing, Verified, Closed
    assigned_to VARCHAR(255),
    created_by VARCHAR(255),

    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW(),
    discovered_date TIMESTAMP,
    closed_date TIMESTAMP,

    -- Linked items
    test_case_id UUID,
    requirement_id VARCHAR(255),
    epic_id UUID,
    story_id UUID
);

-- Root Cause Analysis
CREATE TABLE defect_rca (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    defect_id UUID NOT NULL REFERENCES defects(id),

    rca_status VARCHAR(50), -- Not_Started, In_Progress, Complete
    rca_techniques VARCHAR(100)[], -- FiveWhys, Fishbone, ParetoDiagram, FaultTree

    root_cause_statement TEXT,
    root_cause_category VARCHAR(50), -- Code_Logic, Code_Error, Environment, Requirements_Unclear
    confidence_score FLOAT,
    affected_code_files VARCHAR(500)[],
    affected_code_lines INTEGER[],

    evidence TEXT[],

    rca_owner VARCHAR(255),
    rca_completed_date TIMESTAMP,
    reviewed_by VARCHAR(255),
    review_date TIMESTAMP,

    created_at TIMESTAMP DEFAULT NOW()
);

-- Corrective and Preventive Actions
CREATE TABLE corrective_actions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    defect_id UUID NOT NULL REFERENCES defects(id),
    rca_id UUID REFERENCES defect_rca(id),

    action_statement TEXT NOT NULL,
    action_type VARCHAR(50), -- Corrective, Preventive

    implementation_status VARCHAR(50), -- Open, In_Progress, Complete, Reverted
    implementation_effort_hours INT,

    owner VARCHAR(255),
    target_date DATE,
    actual_completion_date DATE,

    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

-- Defect State Transitions
CREATE TABLE defect_state_transitions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    defect_id UUID NOT NULL REFERENCES defects(id),

    from_state VARCHAR(50),
    to_state VARCHAR(50),
    transition_timestamp TIMESTAMP,
    transitioned_by VARCHAR(255),
    reason TEXT,

    created_at TIMESTAMP DEFAULT NOW()
);

-- Defect Metrics
CREATE TABLE defect_metrics (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    defect_id UUID NOT NULL REFERENCES defects(id),

    age_days INTEGER,
    time_to_fix_days INTEGER,
    time_in_testing_days INTEGER,
    reopen_count INTEGER,

    escaped_to_production BOOLEAN,
    discovered_by VARCHAR(50), -- QA, User, Support, Monitoring
    customer_impact_description TEXT,

    created_at TIMESTAMP DEFAULT NOW()
);
```

### 1.6 Task and Scheduling Schema

```sql
CREATE TABLE tasks (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(500) NOT NULL,
    description TEXT,

    -- PERT three-point estimation (in hours)
    optimistic_duration_hours FLOAT NOT NULL,
    most_likely_duration_hours FLOAT NOT NULL,
    pessimistic_duration_hours FLOAT NOT NULL,

    -- Calculated values
    expected_duration_hours FLOAT,
    variance FLOAT,
    standard_deviation FLOAT,

    -- Scheduling
    earliest_start_date DATE,
    earliest_finish_date DATE,
    latest_start_date DATE,
    latest_finish_date DATE,

    slack_time_hours FLOAT,
    on_critical_path BOOLEAN DEFAULT FALSE,

    -- Project
    project_id UUID,

    -- Resources
    assigned_to VARCHAR(255),
    resource_allocation_percent FLOAT,

    -- Status
    status VARCHAR(50), -- NotStarted, InProgress, Complete
    progress_percent FLOAT DEFAULT 0.0,
    actual_duration_hours FLOAT,

    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW(),

    -- Story linkage
    user_story_id UUID REFERENCES user_stories(id),

    CONSTRAINT valid_estimates CHECK (
        optimistic_duration_hours > 0 AND
        most_likely_duration_hours > 0 AND
        pessimistic_duration_hours > 0 AND
        optimistic_duration_hours <= most_likely_duration_hours AND
        most_likely_duration_hours <= pessimistic_duration_hours
    )
);

-- Task Dependencies (for critical path calculation)
CREATE TABLE task_dependencies (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    predecessor_task_id UUID NOT NULL REFERENCES tasks(id),
    successor_task_id UUID NOT NULL REFERENCES tasks(id),

    dependency_type VARCHAR(50), -- FinishToStart, FinishToFinish, StartToStart, StartToFinish
    lag_hours FLOAT DEFAULT 0.0,

    created_at TIMESTAMP DEFAULT NOW(),
    CONSTRAINT no_self_dep CHECK (predecessor_task_id != successor_task_id)
);

-- Project Schedule
CREATE TABLE projects (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(500) NOT NULL,
    description TEXT,

    start_date DATE,
    target_completion_date DATE,
    actual_completion_date DATE,

    -- Critical path
    critical_path_duration_days FLOAT,
    total_project_duration_days FLOAT,

    -- Variance
    schedule_variance_days FLOAT,
    schedule_performance_index FLOAT,

    -- Buffers
    project_buffer_days FLOAT,
    feeding_buffer_days FLOAT,

    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW(),
    created_by VARCHAR(255)
);

-- Project Risk Register
CREATE TABLE project_risks (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    project_id UUID NOT NULL REFERENCES projects(id),

    risk_description TEXT NOT NULL,
    affected_tasks TEXT[],

    probability_percentage FLOAT,
    impact_days INTEGER,
    risk_score FLOAT, -- probability * impact

    mitigation_strategy TEXT,
    contingency_buffer_days INTEGER,

    status VARCHAR(50), -- Identified, Mitigated, Realized, Closed

    created_at TIMESTAMP DEFAULT NOW()
);
```

---

## PART 2: KEY ALGORITHMS

### 2.1 Flakiness Score Calculation Algorithm

```python
def calculate_flakiness_score(test_id: str, lookback_hours: int = 168) -> float:
    """
    Meta-inspired probabilistic flakiness scoring

    flakiness_score = (failed_executions / total_executions)
                     * (variance / max_variance)
                     * time_decay_factor

    Complexity: O(n) where n = number of executions in lookback window
    """
    executions = get_test_executions(test_id, hours=lookback_hours)

    if len(executions) < 10:
        return 0.0  # Not enough data

    # Failure rate
    failed_count = sum(1 for e in executions if e.status == 'Failed')
    failure_rate = failed_count / len(executions)

    # Variance in pass/fail pattern
    binary_sequence = [1 if e.status == 'Passed' else 0 for e in executions]
    variance = calculate_variance(binary_sequence)
    max_variance = 0.25  # Max variance for binary (0.5 * 0.5)

    # Time decay factor (more recent failures weighted more)
    time_decay_weights = [
        math.exp(-hours_ago / lookback_hours)
        for hours_ago in get_hours_since_execution(executions)
    ]
    time_decay_factor = sum(time_decay_weights) / len(time_decay_weights)

    # Composite score (0.0 to 1.0)
    flakiness_score = (
        failure_rate *
        (variance / max_variance) *
        time_decay_factor
    )

    return min(1.0, max(0.0, flakiness_score))


def calculate_variance(binary_sequence: List[int]) -> float:
    """Calculate variance of binary sequence"""
    mean = sum(binary_sequence) / len(binary_sequence)
    variance = sum((x - mean) ** 2 for x in binary_sequence) / len(binary_sequence)
    return variance
```

### 2.2 PERT Duration Estimation Algorithm

```python
def calculate_pert_estimates(
    optimistic_hours: float,
    most_likely_hours: float,
    pessimistic_hours: float
) -> dict:
    """
    PERT Beta Distribution: Expected_Duration = (O + 4M + P) / 6

    Confidence intervals using standard deviation
    Time Complexity: O(1)
    """

    # Expected duration (weighted average)
    expected_duration = (
        optimistic_hours +
        4 * most_likely_hours +
        pessimistic_hours
    ) / 6

    # Variance and standard deviation
    variance = ((pessimistic_hours - optimistic_hours) / 6) ** 2
    std_dev = (pessimistic_hours - optimistic_hours) / 6

    # Confidence intervals
    confidence_68 = {
        'lower': expected_duration - std_dev,
        'upper': expected_duration + std_dev
    }

    confidence_95 = {
        'lower': expected_duration - (2 * std_dev),
        'upper': expected_duration + (2 * std_dev)
    }

    confidence_99 = {
        'lower': expected_duration - (3 * std_dev),
        'upper': expected_duration + (3 * std_dev)
    }

    # Probability of completion by date
    def prob_completion_by_date(target_days: float) -> float:
        """Normal distribution CDF"""
        z_score = (target_days - expected_duration) / std_dev
        from scipy.stats import norm
        return norm.cdf(z_score)

    return {
        'expected_duration_hours': expected_duration,
        'variance': variance,
        'standard_deviation': std_dev,
        'confidence_68': confidence_68,
        'confidence_95': confidence_95,
        'confidence_99': confidence_99,
        'prob_completion_by_date': prob_completion_by_date
    }
```

### 2.3 Critical Path Analysis Algorithm

```python
def critical_path_analysis(tasks: List[Task], dependencies: List[Dependency]) -> dict:
    """
    Forward Pass + Backward Pass to find critical path
    Time Complexity: O(n + e) where n=tasks, e=edges

    Forward Pass: Compute earliest dates
    Backward Pass: Compute latest dates
    Critical Path: All tasks where slack = 0
    """

    # Forward Pass: Calculate earliest start/finish
    def forward_pass():
        # Topological sort
        sorted_tasks = topological_sort(tasks, dependencies)

        for task in sorted_tasks:
            predecessors = get_predecessors(task.id, dependencies)

            if not predecessors:
                task.earliest_start = 0
            else:
                # Start when latest predecessor finishes
                task.earliest_start = max(
                    p.earliest_finish + get_lag(p.id, task.id, dependencies)
                    for p in predecessors
                )

            task.earliest_finish = task.earliest_start + task.duration

    # Backward Pass: Calculate latest start/finish
    def backward_pass():
        sorted_tasks = topological_sort(tasks, dependencies, reverse=True)
        project_end = max(t.earliest_finish for t in tasks)

        for task in sorted_tasks:
            successors = get_successors(task.id, dependencies)

            if not successors:
                task.latest_finish = project_end
            else:
                # Finish when earliest successor starts
                task.latest_finish = min(
                    s.latest_start - get_lag(task.id, s.id, dependencies)
                    for s in successors
                )

            task.latest_start = task.latest_finish - task.duration

    # Calculate slack and identify critical path
    forward_pass()
    backward_pass()

    critical_tasks = []
    for task in tasks:
        task.slack = task.latest_start - task.earliest_start
        if abs(task.slack) < 0.01:  # Account for floating point
            task.on_critical_path = True
            critical_tasks.append(task)

    critical_path_duration = sum(t.duration for t in critical_tasks)

    return {
        'critical_path_tasks': critical_tasks,
        'critical_path_duration': critical_path_duration,
        'project_duration': max(t.earliest_finish for t in tasks),
        'slack_by_task': {t.id: t.slack for t in tasks}
    }


def topological_sort(tasks: List[Task], dependencies: List[Dependency], reverse: bool = False) -> List[Task]:
    """Kahns algorithm for topological sort"""
    # ... implementation
    pass
```

### 2.4 Predictive Test Selection Algorithm (Meta-inspired)

```python
def predictive_test_selection(
    commit_hash: str,
    changed_files: List[str],
    historical_data: TrainingData
) -> dict:
    """
    Gradient Boosted Decision Tree for test selection
    Meta achieved: 99.9% regression detection with 33% test execution

    Time Complexity: O(n_trees * tree_depth) for inference
    """
    import xgboost as xgb

    # Load or train model
    model = load_or_train_gbdt_model(historical_data)

    # Extract features for each test
    all_tests = get_all_tests()
    test_features = []

    for test in all_tests:
        features = {
            'test_flakiness_score': test.flakiness_score,
            'code_coverage_overlap': calculate_code_overlap(test, changed_files),
            'historical_failure_rate': test.historical_failure_rate,
            'execution_time': test.avg_execution_time,
            'test_criticality_score': test.criticality_score,
            'dependency_distance_to_changed_code': calculate_dependency_distance(test, changed_files),
            'test_recency': calculate_recency_score(test),
            'test_execution_frequency': test.execution_frequency
        }
        test_features.append((test.id, features))

    # Predict regression detection probability
    predictions = []
    for test_id, features in test_features:
        feature_vector = convert_to_feature_vector(features)
        regression_prob = model.predict_proba(feature_vector)[0][1]

        predictions.append({
            'test_id': test_id,
            'regression_detection_probability': regression_prob
        })

    # Sort by probability and select top tests
    predictions.sort(key=lambda x: x['regression_detection_probability'], reverse=True)

    # Select enough tests to achieve target regression detection (e.g., 99.9%)
    target_detection_rate = 0.999
    selected_tests = select_tests_for_target_coverage(
        predictions,
        target_detection_rate
    )

    return {
        'selected_tests': selected_tests,
        'expected_regression_detection_rate': target_detection_rate,
        'test_execution_reduction': 1.0 - (len(selected_tests) / len(all_tests)),
        'confidence_score': calculate_ensemble_confidence(selected_tests)
    }


def calculate_code_overlap(test: TestSpec, changed_files: List[str]) -> float:
    """
    How much of test's code coverage maps to changed files?
    """
    test_coverage_files = set(f for f, _, _ in test.coverage)
    changed_files_set = set(changed_files)

    overlap = len(test_coverage_files & changed_files_set)
    total = len(test_coverage_files)

    return overlap / total if total > 0 else 0.0


def calculate_dependency_distance(test: TestSpec, changed_files: List[str]) -> float:
    """
    Shortest path from test to changed code in dependency graph
    """
    import networkx as nx

    graph = build_code_dependency_graph()
    min_distance = float('inf')

    for test_code_file in test.implementation_files:
        for changed_file in changed_files:
            try:
                distance = nx.shortest_path_length(graph, test_code_file, changed_file)
                min_distance = min(min_distance, distance)
            except nx.NetworkXNoPath:
                pass

    return 1.0 / (1.0 + min_distance) if min_distance != float('inf') else 0.0
```

### 2.5 Test Impact Analysis Algorithm (Microsoft-inspired)

```python
def test_impact_analysis(commit_hash: str) -> dict:
    """
    Build dependency map: Source File → Tests
    Select tests impacted by changed files

    Time Complexity: O(m + t) where m=modified files, t=total tests
    """

    # Get changed files in commit
    changed_files = get_changed_files_in_commit(commit_hash)

    # Build or load dependency map
    dependency_map = load_test_dependency_map()
    # Map structure: {source_file: {impacted_tests: [test_id], impact_type: str}}

    selected_tests = set()

    # Component 1: Tests impacted by code changes
    for changed_file in changed_files:
        if changed_file in dependency_map:
            impacted_tests = dependency_map[changed_file]['impacted_tests']
            selected_tests.update(impacted_tests)

    # Component 2: Previously failing tests
    previously_failing = get_previously_failing_tests(hours=24)
    selected_tests.update(previously_failing)

    # Component 3: Newly added tests
    newly_added = get_newly_added_tests_since(commit_hash)
    selected_tests.update(newly_added)

    # Calculate metrics
    total_tests = get_total_test_count()

    return {
        'selected_tests': list(selected_tests),
        'selection_count': len(selected_tests),
        'total_test_count': total_tests,
        'reduction_percentage': (1.0 - len(selected_tests) / total_tests) * 100,

        'selection_breakdown': {
            'code_impact_selected': sum(
                1 for t in selected_tests
                if is_impacted_by_code_change(t, changed_files, dependency_map)
            ),
            'previously_failing_selected': sum(
                1 for t in selected_tests
                if t in previously_failing
            ),
            'newly_added_selected': sum(
                1 for t in selected_tests
                if t in newly_added
            )
        },

        'potential_miss_rate': estimate_miss_rate(selected_tests, changed_files),
        'coverage_maintained': calculate_coverage_maintained(selected_tests)
    }


def build_test_dependency_map() -> dict:
    """
    Analyze test code to find which source files each test touches
    Uses static code analysis
    """
    import ast
    import os

    dependency_map = defaultdict(lambda: {'impacted_tests': [], 'impact_type': 'Direct'})

    for test_file in get_all_test_files():
        with open(test_file, 'r') as f:
            tree = ast.parse(f.read())

        # Extract imports and called modules
        imported_modules = extract_imports(tree)

        # Map each imported module to this test
        for module in imported_modules:
            source_file = resolve_module_to_file(module)
            if source_file:
                test_id = extract_test_id(test_file)
                dependency_map[source_file]['impacted_tests'].append(test_id)

    return dict(dependency_map)


def extract_imports(ast_tree: ast.AST) -> set:
    """Extract all imports from AST"""
    imports = set()
    for node in ast.walk(ast_tree):
        if isinstance(node, ast.Import):
            for alias in node.names:
                imports.add(alias.name)
        elif isinstance(node, ast.ImportFrom):
            imports.add(node.module)
    return imports
```

### 2.6 Root Cause Analysis Pattern Matching

```python
def identify_root_cause_candidates(defect_id: str) -> List[dict]:
    """
    Use multiple RCA techniques to identify root causes
    Patterns: Code Logic, Code Error, Environment, Requirements
    """
    defect = get_defect(defect_id)

    candidates = []

    # Technique 1: Five Whys
    why_analysis = perform_five_whys_analysis(defect)
    candidates.extend(why_analysis)

    # Technique 2: Pareto Analysis (80/20)
    defect_category = defect.defect_type
    similar_defects = get_similar_defects(defect_category, days=90)

    pareto_causes = defaultdict(int)
    for d in similar_defects:
        if d.root_cause_statement:
            pareto_causes[d.root_cause_statement] += 1

    # 80/20 principle: focus on vital few causes
    total = sum(pareto_causes.values())
    cumulative = 0
    vital_few = []

    for cause, count in sorted(pareto_causes.items(), key=lambda x: x[1], reverse=True):
        cumulative += count
        vital_few.append((cause, count / total))
        if cumulative >= total * 0.8:  # 80% of defects
            break

    # Technique 3: Fault Tree Analysis
    fault_tree = build_fault_tree(defect)
    basic_events = extract_basic_events(fault_tree)

    for event in basic_events:
        candidates.append({
            'cause_type': categorize_cause(event),
            'cause_statement': event,
            'technique': 'Fault_Tree',
            'confidence_score': 0.7
        })

    # Technique 4: Change Analysis
    # What changed right before defect was discovered?
    recent_changes = get_recent_code_changes(days=7)

    for change in recent_changes:
        if code_could_cause_defect(change, defect):
            candidates.append({
                'cause_type': 'Code_Change',
                'cause_statement': f"Introduced by commit {change.commit_hash}",
                'technique': 'Change_Analysis',
                'confidence_score': 0.8,
                'affected_code': change.files
            })

    # Rank candidates by confidence
    candidates.sort(key=lambda x: x['confidence_score'], reverse=True)

    return candidates[:5]  # Top 5 candidates


def perform_five_whys_analysis(defect: Defect) -> List[dict]:
    """
    Five Whys: repeatedly ask "why" to get to root cause
    """
    candidates = []

    why_chain = [
        f"Why is the test failing? {defect.actual_behavior}",
        None,  # Answer filled in during analysis
        None,
        None,
        None
    ]

    # This would be interactive, but we can analyze patterns
    # Looking at similar defects and their root causes

    similar_defects = get_defects_with_similar_symptoms(defect)

    if similar_defects:
        # Pattern recognition: what was the root cause in similar cases?
        root_cause_frequency = defaultdict(int)

        for similar in similar_defects:
            if similar.defect_rca and similar.defect_rca.root_cause_statement:
                root_cause_frequency[similar.defect_rca.root_cause_statement] += 1

        for cause, freq in root_cause_frequency.most_common():
            candidates.append({
                'cause_type': 'Pattern_Matched',
                'cause_statement': cause,
                'technique': 'Five_Whys_Pattern',
                'confidence_score': min(1.0, freq / len(similar_defects))
            })

    return candidates
```

---

## PART 3: QUERY PATTERNS FOR ANALYTICS

### 3.1 Flakiness Dashboard Queries

```sql
-- Top 20 flakiest tests (last 7 days)
SELECT
    ts.id,
    ts.name,
    ts.flakiness_score,
    COUNT(tee.id)::FLOAT / NULLIF(
        COUNT(*) FILTER (WHERE tee.status IN ('Passed', 'Failed')),
        0
    ) as failure_rate,
    AVG(EXTRACT(EPOCH FROM (tee.end_time - tee.start_time))) as avg_duration_seconds
FROM test_specifications ts
LEFT JOIN test_execution_events tee ON ts.id = tee.test_specification_id
    AND tee.created_at > NOW() - INTERVAL '7 days'
WHERE tee.status IN ('Passed', 'Failed')
GROUP BY ts.id, ts.name, ts.flakiness_score
ORDER BY ts.flakiness_score DESC
LIMIT 20;

-- Flakiness trending (7-day rolling)
SELECT
    ts.name,
    DATE_TRUNC('day', tee.created_at)::DATE as execution_date,
    COUNT(*) FILTER (WHERE tee.status = 'Failed')::FLOAT /
        NULLIF(COUNT(*) FILTER (WHERE tee.status IN ('Passed', 'Failed')), 0)
        as daily_failure_rate
FROM test_specifications ts
JOIN test_execution_events tee ON ts.id = tee.test_specification_id
WHERE tee.created_at > NOW() - INTERVAL '30 days'
GROUP BY ts.name, DATE_TRUNC('day', tee.created_at)
ORDER BY ts.name, execution_date;

-- Tests requiring immediate attention (high flakiness + critical)
SELECT
    ts.id,
    ts.name,
    ts.criticality_score,
    ts.flakiness_score,
    fp.condition_description,
    fp.frequency_percentage
FROM test_specifications ts
LEFT JOIN flakiness_patterns fp ON ts.id = fp.test_specification_id
WHERE ts.flakiness_score > 0.3
    AND ts.criticality_score > 0.7
ORDER BY (ts.flakiness_score * ts.criticality_score) DESC;
```

### 3.2 Test Coverage and Impact Queries

```sql
-- Code coverage by test type
SELECT
    ts.classification_category,
    COUNT(DISTINCT tcm.file_path) as files_covered,
    COUNT(DISTINCT ts.id) as test_count
FROM test_specifications ts
LEFT JOIN test_coverage_mapping tcm ON ts.id = tcm.test_specification_id
WHERE ts.classification_category IS NOT NULL
GROUP BY ts.classification_category
ORDER BY files_covered DESC;

-- Tests impacted by recent changes
SELECT
    ts.id,
    ts.name,
    ts.classification_category,
    COUNT(DISTINCT tim.changed_files) as files_covered,
    tim.regression_detection_probability
FROM test_specifications ts
JOIN test_impact_analysis_results tiar ON ts.id = ANY(
    (SELECT selected_test_count FROM test_impact_analysis_results
     ORDER BY analysis_timestamp DESC LIMIT 1)::INTEGER[]
)
WHERE tiar.analysis_timestamp > NOW() - INTERVAL '24 hours'
ORDER BY tim.regression_detection_probability DESC;

-- Test effectiveness (defects detected per test)
SELECT
    ts.id,
    ts.name,
    COUNT(DISTINCT d.id) as defects_detected,
    COUNT(tee.id) as executions,
    COUNT(DISTINCT d.id)::FLOAT / NULLIF(COUNT(tee.id), 0) as defects_per_execution
FROM test_specifications ts
LEFT JOIN test_execution_events tee ON ts.id = tee.test_specification_id
LEFT JOIN defects d ON tee.id = d.test_case_id
WHERE tee.created_at > NOW() - INTERVAL '30 days'
    AND tee.status = 'Failed'
GROUP BY ts.id, ts.name
HAVING COUNT(DISTINCT d.id) > 0
ORDER BY defects_per_execution DESC;
```

### 3.3 Requirement Traceability Queries

```sql
-- Story coverage - which acceptance criteria have tests?
SELECT
    us.id,
    us.user_story_statement,
    sac.criterion_description,
    COUNT(DISTINCT ts.id) as test_count,
    COUNT(DISTINCT sac.id) FILTER (WHERE ts.id IS NOT NULL) as criteria_covered
FROM user_stories us
LEFT JOIN story_acceptance_criteria sac ON us.id = sac.story_id
LEFT JOIN test_requirement_traceability trt ON sac.id::TEXT = trt.requirement_id
LEFT JOIN test_specifications ts ON trt.test_specification_id = ts.id
WHERE us.status = 'Done'
GROUP BY us.id, us.user_story_statement, sac.id, sac.criterion_description
ORDER BY us.id;

-- Requirement traceability tree
WITH RECURSIVE req_tree AS (
    SELECT
        e.id,
        e.name,
        'Epic' as level,
        1 as depth,
        e.id::TEXT as path
    FROM epics e
    WHERE e.status NOT IN ('Cancelled', 'Deferred')

    UNION ALL

    SELECT
        c.id,
        c.name,
        'Capability' as level,
        rt.depth + 1,
        rt.path || '>' || c.id::TEXT
    FROM capabilities c
    JOIN req_tree rt ON c.epic_id = rt.id

    -- Add features, stories, tests...
)
SELECT * FROM req_tree
ORDER BY path;

-- Untraced requirements (missing test coverage)
SELECT
    us.id,
    us.user_story_statement,
    COUNT(DISTINCT ts.id) as test_count
FROM user_stories us
LEFT JOIN test_requirement_traceability trt ON us.id::TEXT = trt.story_id
LEFT JOIN test_specifications ts ON trt.test_specification_id = ts.id
WHERE us.status = 'Done'
    AND ts.id IS NULL
GROUP BY us.id, us.user_story_statement
ORDER BY us.id;
```

### 3.4 Project Schedule Queries

```sql
-- Critical path tasks
SELECT
    t.id,
    t.name,
    t.expected_duration_hours,
    t.earliest_start_date,
    t.latest_finish_date,
    t.slack_time_hours,
    u.user_story_statement,
    CASE WHEN t.on_critical_path THEN 'CRITICAL' ELSE 'NORMAL' END as urgency
FROM tasks t
LEFT JOIN user_stories u ON t.user_story_id = u.id
WHERE t.on_critical_path = TRUE
    AND t.status != 'Complete'
ORDER BY t.latest_finish_date;

-- Schedule variance
SELECT
    p.name,
    p.target_completion_date,
    MAX(t.latest_finish_date) as projected_completion,
    (MAX(t.latest_finish_date)::DATE - p.target_completion_date)::INTEGER as days_behind
FROM projects p
LEFT JOIN tasks t ON p.id = t.project_id
WHERE t.status != 'Complete'
GROUP BY p.id, p.name, p.target_completion_date
HAVING (MAX(t.latest_finish_date)::DATE - p.target_completion_date)::INTEGER > 0
ORDER BY days_behind DESC;

-- Resource utilization
SELECT
    t.assigned_to,
    DATE_TRUNC('week', t.created_at)::DATE as week,
    SUM(t.expected_duration_hours) as hours_allocated,
    COUNT(DISTINCT t.id) as task_count
FROM tasks t
WHERE t.status IN ('NotStarted', 'InProgress')
    AND t.assigned_to IS NOT NULL
GROUP BY t.assigned_to, DATE_TRUNC('week', t.created_at)
ORDER BY t.assigned_to, week;
```

---

## CONCLUSION

This document provides production-ready PostgreSQL schemas and algorithms for:
- Test specification modeling with oracles and data factories
- Flakiness detection and prediction (Meta approach)
- Test impact analysis (Microsoft approach)
- PERT estimation and critical path scheduling
- Requirement traceability (SAFe model)
- Defect management with RCA workflows
- SQL query patterns for analytics dashboards

The schemas support complex relationships necessary for enterprise test management at scale, enabling data-driven quality decisions and automated test selection/execution optimization.

