proffesor-for-testing__agentic-qe/docs/implementation/adrs/ADR-033-early-exit-testing.md

ADR-033: Early Exit Testing

Field	Value
Decision ID	ADR-033
Status	Accepted
Date	2026-01-10
Author	Architecture Team
Review Cadence	6 months

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WH(Y) Decision Statement

In the context of v3 AQE test execution across multi-layer test pyramids (unit, integration, E2E, performance),

facing wasted compute from running all layers when early layers show high confidence, lack of quality signals to distinguish "definitely good" from "maybe good", fixed verification depth regardless of risk level, and inability to predict likely pass/fail outcomes,

we decided for coherence-driven early exit using lambda-stability signals to skip redundant test layers, with speculative execution for remaining layers when confidence thresholds are met,

and neglected fixed-layer execution (always run all layers), ML-based classifiers for exit decisions (more complex, requires training data), and time-based exit criteria (not quality-aware),

to achieve 30-50% compute reduction for high-confidence runs, intelligent test layer skipping based on quality signals, speculative test prediction with >85% accuracy, and <10ms decision latency,

accepting that this requires careful threshold tuning, speculation may occasionally be wrong (hence verification layers), and conservative configurations may reduce savings.

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Context

Current v3 AQE test execution follows a fixed pattern: run all test layers (unit, integration, E2E, performance) without early termination based on quality signals. This approach wastes compute when early layers show high confidence and provides no mechanism to distinguish certain passes from uncertain ones.

RuVector's coherence-driven early exit demonstrates a powerful pattern using lambda stability instead of learned classifiers. High lambda combined with stable lambda-delta indicates confident exit conditions, while low lambda or volatile lambda-delta signals the need to continue to deeper layers.

The early exit pattern enables intelligent test layer skipping where the system exits early when quality signals are confident, reducing overall test execution time while maintaining quality assurance.

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Options Considered

Option 1: Coherence-Driven Early Exit (Selected)

Uses lambda-stability signals from test quality metrics to determine when to exit early. Lambda represents the minimum of pass rate, coverage, and stability metrics. Stability is calculated from lambda-delta between layers.

Pros:

• No ML training required (uses quality signals directly)
• Configurable thresholds for different risk profiles
• Speculative execution provides safety net

Cons:

• Requires threshold tuning per project
• Speculation accuracy depends on exit confidence

Option 2: Fixed-Layer Execution (Rejected)

Always run all test layers regardless of quality signals.

Why rejected: Wastes 30-50% compute on high-confidence runs; no adaptation to quality signals.

Option 3: ML-Based Exit Classifier (Rejected)

Train a classifier to predict when to exit based on historical test data.

Why rejected: Requires significant training data, adds complexity, and may not generalize across projects.

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Dependencies

Relationship	ADR ID	Title	Notes
Part Of	MADR-001	V3 Implementation Initiative	Core testing optimization
Relates To	ADR-030	Coherence-Gated Quality Gates	Shared lambda concepts
Relates To	ADR-032	Time Crystal Scheduling	Test phase coordination

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References

Ref ID	Title	Type	Location
SPEC-033-A	Early Exit Algorithm	Technical Spec	specs/SPEC-033-A-early-exit-algorithm.md
SPEC-033-B	Speculative Execution	Technical Spec	specs/SPEC-033-B-speculative-execution.md
SPEC-033-C	Controller Integration	Implementation Guide	specs/SPEC-033-C-controller-integration.md

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Governance

Review Board	Date	Outcome	Next Review
Architecture Team	2026-01-10	Accepted	2026-07-10

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Status History

Status	Date	Notes
Proposed	2026-01-10	Initial creation from RuVector analysis
Accepted	2026-01-10	Approved by Architecture Team