EMEP KPI Specification: Metrics and Targets

Key performance indicators for EMEP including documentation coverage, traceability, reproducibility, regression rate, throughput, latency, and deployment success metrics.

EMEP measures progress through seven key performance indicators. Each KPI has a clear definition, target value, measurement cadence, and data source. These metrics drive phase gate decisions and continuous improvement.

KPI Registry

KPI	Definition	Target	Measurement Cadence	Source
Documentation Coverage	Percentage of components with complete specifications	100%	Weekly	Spec index vs. component list
Spec-to-Test Traceability	Percentage of specifications with linked test cases	95%	Weekly	Traceability matrix
Reproducibility Score	Percentage of experiments reproducible from artifacts	100%	Per experiment	ExperimentTracker
Regression Rate	Percentage of candidates showing regression on Validation Set	Below 5%	Per generation	EvaluationEngine
Mean Candidate Throughput	Candidates evaluated per GPU-hour	Above 2.0	Daily	EvolutionEngine
Mean Evaluation Latency	Wall-clock time per benchmark run	Below 30 min	Daily	BenchmarkEngine
Deploy Success Rate	Percentage of deployments completing without rollback	Above 99%	Per deployment	DeploymentManager

Measurement Details

Documentation Coverage

Calculated as: (specified components / total components) * 100. A component is specified when it has a merged page in the documentation site with complete frontmatter, opening prose, and at least one diagram. Target is 100% by end of Phase 0.

Spec-to-Test Traceability

Calculated as: (specs with linked tests / total specs) * 100. A link requires a bidirectional reference: the spec page cites a test ID, and the test references the spec path. Target is 95% by end of Phase 2, 100% by end of Phase 5.

Reproducibility Score

An experiment is reproducible when another operator can re-run it from stored artifacts and obtain the same candidate status (PASS, FAIL, REGRESSION, INVALID, INCOMPLETE) within a tolerance band. The score is: (reproducible experiments / total experiments) * 100. Target is 100% for all experiments tracked in ExperimentTracker.

Regression Rate

Calculated per generation: (REGRESSION candidates / total evaluated candidates) * 100. Regression means a candidate scores lower than its parent on any Validation Set benchmark. Target is below 5% after Phase 5 stabilization.

Mean Candidate Throughput

Candidates fully evaluated (all benchmarks, all splits) per GPU-hour. Measured on A100-equivalent hardware. Target is above 2.0 by end of Phase 5. This KPI is an Engineering assumption.

Mean Evaluation Latency

Wall-clock time from benchmark job submission to result availability, averaged over a 24-hour window. Target is below 30 minutes per benchmark by end of Phase 4.

Deploy Success Rate

Percentage of DeploymentManager executions that complete without triggering rollback. Calculated as: (successful deployments / total deployments) * 100. Target is above 99% by end of Phase 7.

Evidence Tags

Tag	Application
Engineering assumption	Candidate throughput target assumes A100-class GPUs and fp16 inference
Future goal	100% reproducibility requires deterministic GPU operations, which may need CUDA flags
Research hypothesis	Regression rate below 5% assumes effective multi-objective optimization

KPI targets are baselines for phase gates, not guarantees. If a target is missed, the phase gate requires a documented mitigation plan before proceeding.