Test Coverage Analysis and Optimization Questions

Reasonable targets (practical, not dogmatic):

• Unit tests: 60–80% — cover feature transformations, utility functions, schema checks, and small model components.
• Integration tests: 30–50% — cover feature ingestion → preprocessing → model scoring pipeline (including storage and message brokers).
• End-to-end (E2E) tests: 5–15% — validate the full REST API flow: request → feature lookup/ingest → scoring → response and error paths.

Which components need higher coverage and why:

• Feature ingestion & preprocessing: high coverage (unit + integration) because data bugs silently corrupt model inputs. Tests should assert schemas, missing/NULL handling, scaling and categorical mappings.
• Model scoring code: high unit/integration coverage to ensure deterministic inference, correct postprocessing, and versioned model loading.
• REST API: thorough integration and E2E tests for authentication, rate limits, error handling, and latency SLAs; smaller unit surface for controllers.
• Glue and infra (connectors, retries): integration tests and chaos/fault-injection to validate resilience.

Why raw % is insufficient:

• Coverage is about lines executed, not behavior. 100% can still miss wrong assumptions, data drift, numerical instability, or adversarial inputs.
• Emphasize test quality: property-based tests, adversarial/edge-case datasets, model regression tests (performance/metrics), offline A/B tests, and production monitoring (schema alerts, drift, latency).
• Use a test pyramid: many fast unit tests, fewer integration tests, minimal E2E. Combine coverage metrics with test effectiveness metrics (bug escape rate, mutation testing, stability) to judge production readiness.