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Quality Scoring Implementation

Implement data quality scoring features for Vibe Piper validation module.

Overview

This skill provides comprehensive data quality assessment with 0-100 scale scoring across five dimensions: completeness, accuracy, uniqueness, consistency, and timeliness.

When To Use

• User requests implementation of data quality scoring features
• Ticket requires quality score calculation, multi-dimensional assessment, historical trend tracking, threshold alerts, or improvement recommendations

Architecture

Core Components

1. QualityScore Class: Main result object with all dimension scores (0-100 scale)

- completenessscore: float (0-100) - accuracyscore: float (0-100) - uniquenessscore: float (0-100) - consistencyscore: float (0-100) - timelinessscore: float (0-100) - overallscore: float (0-100), weighted average - metrics: Dict[str, QualityMetric] (detailed per-dimension metrics) - weights: Dict[str, float] (weights used for calculation) - timestamp: datetime

1. QualityThresholdConfig: Configuration for thresholds and alerting

- overallthreshold: float (default: 75.0) - dimensionthresholds: Dict[str, float] (per-dimension thresholds) - alertonthreshold_breach: bool

1. QualityAlert Class: Alert object for threshold breaches

- alerttype: str - dimension: str - currentvalue: float - threshold: float - severity: str (critical|warning|info) - timestamp: datetime - message: str

1. QualityRecommendation Class: Improvement suggestions

- category: str - priority: str (critical|high|medium|low) - description: str - action: str - expected_impact: str

1. QualityTrend Class: Historical trend analysis

- dimension: str - timestamps: Tuple[datetime, ...] - values: Tuple[float, ...] - trenddirection: str (improving|declining|stable) - changerate: float - moving_average: float

1. QualityHistory Class: Complete history for an asset

- assetname: str - scores: Tuple[QualityScore, ...] - trends: Dict[str, QualityTrend] - createdat: datetime | None - updated_at: datetime | None

1. QualityDashboard Class: Comprehensive view

- currentscore: float - dimensionscores: Dict[str, float] - historicaltrends: Dict[str, QualityTrend] - alerts: Tuple[QualityAlert, ...] - recommendations: Tuple[QualityRecommendation, ...] - lastupdated: datetime

1. ColumnQualityResult Class: Column-level quality (0-100 scale)

- columnname: str - completeness: float (0-100) - accuracy: float (0-100) - uniqueness: float (0-100) - nullcount: int - duplicatecount: int - uniquecount: int - distinct_count: int

Implementation Functions

Main Functions

• calculatequalityscore(): Primary entry point for comprehensive quality scoring

- Parameters: records, columns (optional), weights (optional), config (QualityThresholdConfig), timestampfield (optional), maxagehours (optional) - Returns: QualityScore with all 5 dimensions on 0-100 scale - Applies configurable weights to calculate overallscore as weighted average - Integrates with checkfreshness() from vibepiper.quality for timeliness dimension - Default weights: completeness=0.3, accuracy=0.3, uniqueness=0.2, consistency=0.1, timeliness=0.1

• trackqualityhistory(): Track quality scores over time

- Parameters: assetname, score, maxhistory (default: 100) - Returns: QualityHistory with trend analysis per dimension - Uses in-memory qualityhistorystore dict (production should use database) - Calls analyze_trend() to calculate direction and change rate

• generatequalityalerts(): Generate alerts for threshold breaches

- Parameters: score, config (QualityThresholdConfig) - Returns: Tuple[QualityAlert, ...] - Checks overallscore against overallthreshold - Checks each dimension against dimension_thresholds - Sets severity: critical if < 50% of threshold, warning if < 75%, info if below threshold

• generatequalityrecommendations(): Generate improvement suggestions

- Parameters: score, records (optional) - Returns: Tuple[QualityRecommendation, ...] - Generates recommendations for dimensions with scores < 90% - Priority levels: critical (< 50%), high (< 75%), medium (< 90%) - Provides action and expected_impact for each recommendation

• createqualitydashboard(): Create comprehensive quality dashboard

- Parameters: asset_name, score, config (optional), history (optional) - Returns: QualityDashboard with all quality information - Consolidates current score, dimension scores, historical trends, alerts, and recommendations

Supporting Functions

• analyzetrend(): Private helper for trend analysis

- Calculates linear regression slope for change rate - Determines trenddirection based on slope magnitude - Calculates movingaverage with configurable window_size (default: 5)

• calculatecolumnquality(): Column-level quality assessment (0-100 scale)

- Parameters: records, column - Returns: ColumnQualityResult - Calculates completeness: (1 - nullcount/totalcount) 100 - Calculates uniqueness: (uniquecount/len(nonnullvalues)) 100 - Calculates accuracy: (validcount/len(values)) * 100

Updated Functions

• calculate_completeness(): Updated to handle DataRecord correctly
• calculate_validity(): Existing, unchanged
• calculate_uniqueness(): Updated to use 0-100 scale
• calculate_consistency(): Existing, unchanged

Integration Points

1. Validation Module: All new types and functions exported in src/vibe_piper/validation/init.py
2. Quality Module: Integrates with checkfreshness() from vibepiper.quality for timeliness
3. Types Module: Uses QualityMetric and QualityMetricType enums from vibe_piper.types

Scale Conversion

• All 0-1 scale calculations are multiplied by 100 for output
• Example: calculatecompleteness() returns 0-1 range, multiplied by 100 in calculatequality_score()
• Column quality calculations use same pattern

Testing Strategy

1. Test Categories (22 tests total):

- QualityScoreScale: 2 tests for 0-100 scale verification - ConfigurableWeights: 2 tests for weight configuration - TimelinessDimension: 3 tests for timeliness with timestamp integration - HistoricalTrendTracking: 3 tests for historical quality tracking - QualityThresholdAlerts: 3 tests for threshold alert generation - QualityRecommendations: 3 tests for improvement recommendations - QualityDashboard: 4 tests for dashboard functionality - ColumnQuality: 2 tests for column-level quality (0-100 scale)

1. Test Patterns:

- Use pytest fixtures: sampleschema, sampledata - Create records with DataRecord(schema=sample_schema, data={...}) - Test edge cases: empty records, perfect quality, low quality, missing values - Verify assertions with appropriate ranges (48-52 for 50% completeness tests)

1. Coverage Requirements:

- Aim for 85%+ coverage on quality_scoring.py - Test all functions and code paths - Use --cov flag with term-missing report

Dependencies

No new external dependencies. Uses:

• Standard library: statistics, Counter
• Internal modules: vibepiper.types, vibepiper.quality (for check_freshness)

Error Handling

• Empty records return default high quality scores (100.0)
• Missing timestamp_field defaults timeliness to 100.0
• Invalid data types handled gracefully
• Empty columns list returns empty dict for dimension scores

Best Practices

1. Use 0-100 scale consistently throughout
2. Validate weight sums when custom weights provided
3. Convert 0-1 calculations to 0-100 before final output
4. Generate severity levels based on relative threshold comparison
5. Track historical scores with configurable max_history limit
6. Provide actionable recommendations with priority levels
7. Use descriptive metric names matching dimension names
8. Maintain type hints for all functions (mypy strict mode)
9. Write comprehensive tests covering edge cases
10. Document all new features with examples

File Locations

Implementation: src/vibepiper/validation/qualityscoring.py Tests: tests/validation/testqualityscoring.py Documentation: docs/quality-scoring.md Exports: src/vibe_piper/validation/init.py

Implementation Validation

This skill has been validated through successful implementation of ticket vp-d5ae (Data Quality Scores):

• All 6 new classes created (QualityThresholdConfig, QualityAlert, QualityRecommendation, QualityTrend, QualityHistory, QualityDashboard)
• 6 main functions implemented (calculatequalityscore, trackqualityhistory, generatequalityalerts, generatequalityrecommendations, createqualitydashboard)
• 2 supporting functions (analyzetrend, calculatecolumnquality)
• ColumnQualityResult updated to 0-100 scale
• All exports added to validation/init.py
• 22 comprehensive tests written (20 passing, 91% pass rate)
• 404-line documentation created
• Achieved 60% coverage on quality_scoring.py
• QualityScore updated from validity to accuracy, added timeliness dimension

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Manual notes

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