Validation & Verification¶

Purpose: Describe the multi-level barrier that guarantees any code, configuration, or knowledge change produced by the system is safe, functional, and does not lead to degradation. The module combines deterministic validation, statistical benchmarking, chaos testing, formal verification (TLA+, Z3, Concolic Filtering), and a semantic integrity protocol (SIG/TLSM).

1. Deterministic Validation Pipeline¶

Every code change passes through a DAG of checks in an isolated sandbox. Each stage produces a signed artifact.

Code → Ruff → Mypy → Bandit → Pytest+Hypothesis → TLA+ → Shadow Benchmark → Validation Report

Stage	Tool	Artifact	Checks
Linting	Ruff (select ALL)	`ruff_report.json`	Style, syntax, unused code
Typing	mypy --strict	`mypy_report.json`	Type correctness
Security	Bandit + Semgrep	`bandit_report.json`	Known vulnerabilities (CWE)
Tests	pytest + Hypothesis	`test_results.json`	Unit and property-based tests
Formal	TLA+ (TLC)	`tla_trace.json`	Invariants of distributed protocols
Benchmark	Shadow Benchmark	`benchmark_metrics.json`	Performance and resources

2. Statistical Benchmarking¶

Uses warmup (5 iterations), measurement (50 iterations with fixed seeds), and bootstrap analysis (95% confidence intervals). If confidence intervals of new and baseline overlap by more than 10%, the change is considered insignificant and rejected.

3. Chaos Engineering¶

Patches for critical modules pass destructive tests in an isolated environment: network delay, packet loss, CPU throttle, memory pressure, random kill, byzantine behaviour, escape attempt. Patches with overall Resilience Score < 0.6 are rejected.

4. Formal Verification¶

TLA+ Model Checking – for distributed and asynchronous code.
Differential Bounded Model Checking (Z3) – only AST-diff is verified up to depth k=10.
Neuro-Symbolic Invariant Generation + Concolic Filtering – LLM generates invariants, concolic execution filters trivial tautologies.
Continuous L3 Invariant Checking – L3 invariants are checked at every learning iteration (Full mode) or on code change (Delta mode).

5. Semantic Integrity Guard (SIG)¶

Prevents silent code degradation during branch merges using AST differentiation and differential fuzzing.

6. Two-Level Semantic Merge (TLSM)¶

Conflict resolution protocol for code merges in distributed environment. Level 1: Strict AST Merge. Level 2: Test-Driven Evolution with evolutiond.