Assurance

PreFlight — Deterministic Upstream Data Ingestion Gate

Verified as of 3 July 2026.

Assurance Scope

Assurance verifies deterministic enforcement behavior of the PreFlight v1.01 system.

Verification applies exclusively to:

• Evaluation determinism and mechanical verdict reduction integrity
• Behavioral invariant verification under generated inputs
• Structural robustness under adversarial and malformed byte streams
• Real-world dataset validation against documented verdict outcomes
• Line-level validation coverage of the enforcement engine package
• Behavioral mutation testing of enforcement decision logic
• Static structural integrity, static type checking, security surface analysis, and dependency exposure review

Scope is limited strictly to enforcement system behavior.

Hosted infrastructure, deployment environments, operational configuration, and external integrations are outside the v1.01 assurance boundary.

Deterministic Enforcement Integrity

• Evaluation order is immutable
• Verdict reduction is mechanical
• Identical inputs yield identical outcomes
• No probabilistic constructs exist
• No adaptive logic exists

Validation confirms deterministic upstream ingestion enforcement behavior across the full evaluation path.

PRE-FLIGHT ASSURANCE SUMMARY — v1.01

Scope:
Enforcement system behavior only
Hosted infrastructure excluded

Verification Domains:
- Deterministic evaluation integrity
- Behavioral invariant verification (property-based)
- Structural robustness / bounded fuzz
- Real-world dataset validation
- 100% statement coverage (preflight_core)
- Behavioral mutation testing
- Static analysis, type checking, security review, dependency audit

Validation Model:
Integration, property, fuzz, corpus, coverage, and mutation verification
aligned with ingestion enforcement architecture.
  

Behavioral Invariant Verification

Hypothesis property tests exercise run_preflight under generated tabular inputs and random byte blobs.

• Every run returns a valid verdict in the fixed set
• Identical inputs produce byte-identical canonical JSON reports
• Verification confirms that structurally corrupt input is not issued a PASS verdict.
• Random byte payloads do not crash the evaluation path within test budgets

Invariants hold across delimiter variants, row/column bounds, and cell-type mixes within test budgets.

PRE-FLIGHT VETTING SUMMARY — PROPERTY-BASED INVARIANTS

Tooling: Hypothesis (pytest)
Model: Generated CSV tables + random byte blobs

Scope:
run_preflight enforcement path

Verification:
- Valid verdict on all generated inputs
- Deterministic canonical JSON reproducibility
- Fail-closed: structurally corrupt input not issued PASS
- No unhandled exceptions on arbitrary bytes within test budgets

Conclusion:
Behavioral invariants hold within defined generation bounds.
  

Structural Robustness

Bounded fuzzing hammers the engine with random byte streams within wall-clock and size limits.

• Every input must return a structurally valid report
• Verdict must remain within PASS / WARN / FAIL / ANALYSIS_INCOMPLETE
• No crashes, hangs, or unhandled exceptions within the fuzz budget

Robustness verification complements property tests with non-tabular adversarial input.

PRE-FLIGHT VETTING SUMMARY — ROBUSTNESS / FUZZ

Tooling: pytest (bounded random-byte harness)
Model: ~300 iterations, ≤128 KiB per payload, 180s wall clock

Scope:
run_preflight on arbitrary byte content

Verification:
- No crashes across fuzz budget
- Valid verdict contract on every iteration

Conclusion:
Enforcement path remains stable under adversarial byte input.
  

Real-World Validation

Documented production-scale datasets are run through the frozen v1.01 engine and compared to published verdict outcomes on the Examples page.

• PASS, WARN, FAIL, and ANALYSIS_INCOMPLETE cases represented
• Verdicts and triggering tests reconciled against current engine output
• Empirical determinism confirmed by repeat runs on fixed fixtures

Corpus validation exercises the full seven-test evaluation path on real-world structural conditions.

PRE-FLIGHT VETTING SUMMARY — REAL-WORLD VALIDATION

Model: Documented dataset corpus + Examples page artifacts

Scope:
Production-scale CSV fixtures across all four verdict classes

Verification:
- Final verdict matches published examples (current engine)
- Triggering tests and evidence paths exercised end-to-end
- Repeat runs produce identical outcomes

Conclusion:
Real-world structural conditions align with documented enforcement outcomes.
  

Validation Coverage

Statement coverage of the enforcement engine is complete (100%), reported without suppression. Coverage confirms exercise, not sufficiency.

• Measured on the preflight_core enforcement package as part of the step10 validation suite
• Coverage exercised through integration, property, fuzz, and targeted branch tests
• Complements determinism, property, fuzz, and mutation verification — does not replace them

PRE-FLIGHT VETTING SUMMARY — COVERAGE

Tooling: coverage.py (pytest)
Model: Statement coverage on preflight_core

Objective:
Confirm every enforcement statement is exercised by the validation suite

Result:
100% statement coverage, reported without suppression

Conclusion:
Coverage confirms exercise, not sufficiency.
  

Mutation Verification

Mutation testing was applied to the enforcement decision logic to confirm the verification suite detects behavioral change, not only line execution. Identified gaps were closed; mutations with no behavioral effect are documented as equivalent.

PRE-FLIGHT VETTING SUMMARY — MUTATION

Tooling: cosmic-ray
Model: Behavioral mutation against step10 pytest suite

Scope:
preflight_core enforcement decision logic

Verification:
- Competent mutants killed by behavioral tests
- Identified gaps closed
- Equivalent mutations documented

Conclusion:
Verification suite detects behavioral change, not only line execution.
  

Static, Type, Security, and Dependency Verification

Static Structure

• No structural violations identified
• No stochastic constructs present
• No dynamic evaluation mechanisms present
• Codebase constrained to bounded deterministic evaluation

PRE-FLIGHT VETTING SUMMARY — STATIC ANALYSIS

Tool: Ruff
Scope: v1.01 enforcement codebase

Result:
No structural violations detected

Conclusion:
Codebase integrity consistent with bounded deterministic evaluation.
  

Type Checking

• Static type analysis on enforcement modules
• Type consistency across evaluation and reduction paths
• No unresolved type errors in enforcement scope

PRE-FLIGHT VETTING SUMMARY — TYPE CHECKING

Tool: mypy
Scope: preflight_core enforcement package

Result:
No type errors in enforcement modules

Conclusion:
Static types consistent with bounded deterministic evaluation surface.
  

Security Surface

• No dynamic code evaluation
• No interactive system surface
• No unsafe evaluation constructs identified by static analysis
• No injection primitives detected within enforcement scope

PRE-FLIGHT VETTING SUMMARY — SECURITY ANALYSIS

Tool: Bandit
Scope: v1.01 enforcement codebase

Result:
No unsafe evaluation constructs identified

Conclusion:
Evaluation surface verified as mechanically bounded.
  

Dependency Exposure

• Advisories identified with remediation paths
• No dynamic network interaction within v1.01 enforcement boundary
• Exposure limited to system environment context

PRE-FLIGHT VETTING SUMMARY — DEPENDENCY AUDIT

Tool: pip-audit
Scope: Runtime dependencies

Result:
Advisories identified with defined remediation paths

Exposure Context:
- System operates within bounded evaluation surface
- No dynamic network interaction in v1.01 scope

Conclusion:
Dependency exposure managed within defined enforcement boundary.
  

Assurance Boundary

Assurance confirms deterministic ingestion enforcement integrity only.

Not evaluated:

• Business correctness
• Semantic validity
• Workflow orchestration
• Hosted infrastructure
• Deployment hardening
• Operational governance

Assurance reflects mechanical enforcement integrity within defined scope.