AIPCH14 — Testable & Versioned

“Version-Controlled and Behaviorally Testable”

What AIPCH14 is really asserting​

AIPCH14 is not asserting that:

“The AI Product has versions or some test cases.”

It is asserting that:

The AI Product evolves through explicit, versioned states where each version is protected by reproducible, behavior-focused tests that validate its intended functionality, constraints, and outcomes — ensuring safe, controlled, and auditable evolution over time.

Versioning is not labeling.
Testing is not validation once.

This is about controlled evolution of intelligence.

The Essence (HDIP + AIPS Interpretation)​

An AI Product is testable and versioned if and only if:

1. Every change results in a new, explicitly versioned product state
2. Each version is validated through reproducible, behavior-driven tests
3. Evolution preserves:
   • intent (AIPCH13)
   • semantics (AIPCH12)
   • constraints (AIPCH10)

If changes:

• are implicit
• cannot be reproduced
• are not validated against expected behavior

then AIPCH14 is not met, even if version numbers exist.

What Must Be Versioned​

Versioning must apply to the entire AI Product, including:

1. Behavior​

• decision logic
• output patterns
• response characteristics

2. Specification​

• AIPROD (intent, semantics, constraints)
• AIPDS (deployment realization)

3. Dependencies​

• underlying models
• data sources
• composed AI/Data Products

👉 Versioning is product-level, not component-level.

What Must Be Tested​

Testing must validate:

1. Behavioral correctness​

• expected outputs for given inputs
• consistency of decisions
• adherence to intent

2. Constraints and policies​

• compliance rules
• safety boundaries
• risk thresholds

3. Quality expectations​

• accuracy or performance thresholds
• latency expectations (AIPCH09)
• reliability

4. Regression safety​

• no unintended degradation
• no semantic drift
• no violation of prior guarantees

👉 Testing is:

behavioral, not just technical

Positive Criteria — When AIPCH14 is met​

AIPCH14 is met when all of the following are true:

1. Versions are explicit and traceable​

Each version:

• is uniquely identifiable
• linked to:
  • specification (AIPROD)
  • deployment (AIPDS)
• includes change history

There is full provenance of evolution.

2. Tests are reproducible and automated​

The system:

• executes tests automatically
• produces consistent results
• does not rely on manual validation

Tests are:

• versioned
• repeatable

3. Tests validate behavior, not just components​

Tests focus on:

• what the AI Product does
• not just:
  • model accuracy
  • pipeline correctness

This ensures:

product-level validation

4. Version changes are gated by validation​

New versions:

• must pass defined tests
• cannot be promoted without validation
• are evaluated against prior versions

This ensures:

safe evolution

5. Consumers are protected from breaking changes​

Versioning ensures:

• backward compatibility (where required)
• clear communication of changes
• ability to select or remain on versions

This supports:

stable consumption (AIPCH11)

Negative Criteria — When AIPCH14 is not met​

AIPCH14 is not met if any of the following are true:

❌ Changes are implicit or uncontrolled​

Examples:

• model updated without version change
• behavior changes without tracking
• no clear history

This creates unpredictability.

❌ Testing is manual or ad hoc​

Examples:

• manual validation before release
• no automated test suite
• inconsistent testing

This is unreliable.

❌ Tests focus only on components​

Examples:

• model accuracy only
• pipeline execution only
• no validation of:
  • decisions
  • outcomes
  • constraints

This misses product behavior.

❌ No regression validation exists​

Examples:

• no comparison with previous versions
• degradation unnoticed
• behavior drift untracked

This creates risk.

❌ Consumers are exposed to breaking changes​

Examples:

• interfaces change without versioning
• outputs change unexpectedly
• no control over version adoption

This breaks trust.

Edge Cases (Important Guidance for Agents)​

Case 1: “Model versioned but product not versioned”​

❌ Not met

Rationale:

• component-level versioning only
• product behavior not tracked

Case 2: “Tests exist but not reproducible”​

⚠️ Partial

Rationale:

• validation exists
• but not reliable or consistent

Case 3: “Full product versioning + automated behavioral tests”​

✅ Met

Rationale:

• safe, controlled evolution
• reproducibility ensured

Case 4: “Continuous learning without version control”​

❌ Not met

Rationale:

• behavior changes without traceability
• unsafe and ungovernable

Evidence Signals an Agent Should Look For​

Authoritative evidence:​

• version identifiers linked to AIPROD/AIPDS
• automated test suites
• test results per version

Supporting evidence:​

• change logs
• regression reports
• version selection capability for consumers

Red flags:​

• lack of version history
• manual testing processes
• unexplained behavior changes
• no regression validation

How an Agent Should Decide​

Decision rule (simplified):

If the AI Product cannot evolve through clearly versioned states validated by reproducible, behavior-focused tests that protect against unintended changes, AIPCH14 is not met.

Why AIPCH14 Is Non-Negotiable​

Without AIPCH14:

• behavior becomes unpredictable
• trust (AIPCH07) degrades
• governance (AIPCH10) cannot be enforced reliably
• reuse (AIPCH08) becomes unsafe

AIPCH14 enables:

• safe and controlled evolution of AI Products
• reproducibility and auditability
• consumer trust in stability
• continuous improvement without risk

Canonical Statement (for AIPS)​

AIPCH14 is satisfied only when an AI Product evolves through explicitly versioned states, each validated by reproducible, behavior-focused tests that ensure consistency, constraint adherence, and safe progression without unintended degradation or breaking changes.