Below is a full, detailed AI Stack Test Plan you can use as a real project document. It is structured to cover six layers end‑to‑end: Hardware, System Software, Data Pipeline, Model, Application, and Deployment & MLOps. It includes objectives, scope, environments, entry/exit criteria, test cases, metrics/SLOs, tooling, automation gates, risk management, defect workflow, RACI, and traceability.

If you want, I can also generate this as a .docx or .xlsx with tables and checklists ready to use.

AI Stack Test Plan

Owner: Minh Do Anh Audience: Engineering, QA, Data/ML, SRE, Security, Product Revision: v1.0 Status: Draft Applies to: Training & Inference, Cloud & Edge, Batch & Real-time

1) Purpose & Goals

Purpose: Ensure the AI system is correct, robust, secure, performant, cost-efficient, and compliant from silicon to application.

Top-level quality attributes:

• Functional correctness (models & app behaviors)
• Reliability (availability, resiliency, graceful degradation)
• Performance (latency/throughput, GPU utilization)
• Safety & Security (prompt injection, data leakage, access control)
• Responsible AI (bias/fairness, toxicity, privacy)
• Scalability & Cost (autoscaling, budgets)
• Maintainability & Operability (monitoring, rollback, observability)

2) Scope

In scope:

• Hardware (AI accelerators) qualification & performance
• System software (drivers, firmware, kernels, CUDA/ROCm/DirectML stacks)
• Data pipeline (ingest, validation, ETL, feature store, lineage)
• Model development (training, evaluation, robustness, reproducibility)
• Application layer (APIs, UX, guardrails, safety)
• Deployment & MLOps (CI/CD, infra, registries, rollouts, monitoring)

Out of scope (v1):

• Physical facility tests (power, HVAC) beyond thermal load tests
• Long-term user studies (covered by product research)

Assumptions & Dependencies:

• Access to test environments (dev/stage/prod-like)
• Representative datasets and synthetic data generators available
• Model registry and feature store in place
• Observability stack (logs, metrics, traces) configured

3) Test Environments

Config matrix (example): GPU types (A100/H100/MI300), drivers (nvidia‑driver xx.xx), CUDA/ROCm versions, kernel versions, container runtime, NCCL/RDMA fabric versions.

4) Entry/Exit Criteria

General Entry

• Requirements & risks documented
• Test data prepared and approved
• Environments provisioned and baseline verified
• Monitoring & logging enabled

General Exit

• Critical/High defects: 0 open
• Medium defects: ≤ agreed threshold with mitigations
• All acceptance tests pass; performance within SLOs
• Rollback and disaster recovery validated

5) Test Strategy by Layer

5.1 Hardware Layer (AI Chips / Accelerators)

Objectives

• Validate compute correctness, performance, thermal behavior, and stability under sustained load.
• Verify compatibility with host platforms and interconnects (PCIe/NVLink/InfiniBand).

Test Types & Example Test Cases

• Functional
  • HW‑F01: GEMM/tensor ops produce expected outputs (tolerance ≤ 1e‑6).
  • HW‑F02: Mixed precision (FP16/BF16/FP8) correctness vs. FP32 baseline.
• Performance
  • HW‑P01: Measure TFLOPS with standard kernels (cuBLAS/cuDNN).
  • HW‑P02: Memory bandwidth & cache hit rates; p99 inference latency at target QPS.
• Stress & Soak
  • HW‑S01: 24‑48h sustained training load; no ECC errors beyond threshold.
  • HW‑S02: Thermal throttling behavior; stable clocks under max load.
• Compatibility
  • HW‑C01: Multi‑GPU topology detection; NCCL all‑reduce across nodes.
  • HW‑C02: Power limit profiles and graceful degradation.

Tools: vendor diagnostics, nvidia-smi, Nsight, NCCL tests, perf counters. Metrics/SLOs: p95 latency, GPU util ≥ 85% under load, error rate (ECC) ≤ agreed threshold. Risks & Mitigation: Thermal limits → add airflow tests & alerts; fabric issues → redundant paths.

5.2 System Software Layer (Drivers, Firmware, Runtimes)

Objectives

• Ensure stable, performant, and correct interaction between OS, drivers, runtimes (CUDA/ROCm/DirectML), and frameworks (PyTorch/TensorFlow).

Test Types

• Install/Upgrade/Rollback
  • SS‑I01: Clean install across supported OS versions.
  • SS‑I02: Safe rollback with preserved settings.
• API/ABI Compliance
  • SS‑A01: CUDA/ROCm APIs behavior matches spec; kernel launches succeed.
  • SS‑A02: Mixed framework versions (PyTorch/TensorFlow) on given runtime combinations.
• Resilience
  • SS‑R01: GPU reset/recovery mid‑training; job resumes/aborts gracefully.
  • SS‑R02: OOM handling produces actionable errors; no host crash.
• Perf Regressions
  • SS‑P01: Kernel performance benchmarks per release; alert on >3% regression.

Tools: CI scripts, containerized matrix tests, kernel profilers. SLOs: No perf regression >3% across supported stacks; zero kernel panics. Risks: Version skew → pin images; SBOM + provenance checks.

5.3 Data Pipeline

Objectives

• Guarantee data quality, integrity, privacy, lineage, and scalability across ingestion, validation, transformation, and feature serving.

Test Types

• Schema & Contract Validation
  • DP‑S01: Enforce schemas (types/ranges/uniqueness) on ingest.
  • DP‑S02: Backward/forward compatibility checks (data contracts).
• Quality & Anomalies
  • DP‑Q01: Missing values, outliers, drift (PSI/KL divergence).
  • DP‑Q02: Duplicate detection and de‑skewing.
• Transformation Correctness
  • DP‑T01: Deterministic ETL; reference outputs match golden datasets.
  • DP‑T02: Feature leakage detection (no target leakage).
• Security & Privacy
  • DP‑P01: PII detection & masking; encryption at rest/in transit verified.
  • DP‑P02: Access controls (RBAC/ABAC) enforced; audit logs present.
• Reliability & Throughput
  • DP‑R01: Backfill jobs; idempotency on retries; exactly‑once where required.
  • DP‑R02: SLA on batch window completion and streaming end‑to‑end latency.

Tools: Great Expectations, Deequ, TFX, Airflow/Prefect/Dagster tests, DVC/LakeFS, Monte Carlo/WhyLabs for data observability, Soda/Amundsen/DataHub for lineage. Metrics/SLOs: Data validation pass rate ≥ 99.5%, end‑to‑end pipeline latency SLO, drift alerts ≤ x/month with triage SLA. Risks: Silent data drift → add canary datasets & drift monitors; schema evolution → contract negotiation process.

5.4 Model Layer (Training & Evaluation)

Objectives

• Validate correctness, performance, robustness, fairness, and reproducibility of models.

Test Types

• Unit & Integration
  • ML‑U01: Layer/ops unit tests; gradient checks.
  • ML‑U02: Dataloader determinism; seed control; mixed precision correctness.
• Training Dynamics
  • ML‑T01: Loss convergence within N epochs; no divergence/NaNs.
  • ML‑T02: Early stopping/regularization prevents overfitting (train–val gap).
• Evaluation & Calibration
  • ML‑E01: Metrics (accuracy/F1/AUC/BLEU/ROUGE/MAP) hit targets on hold‑out.
  • ML‑E02: Calibration (ECE/Brier); confidence thresholds tuned.
• Robustness & Safety
  • ML‑R01: OOD tests; adversarial robustness (FGSM/PGD where relevant).
  • ML‑R02: Fairness metrics across slices; toxicity/hate/offensive filters for LLMs.
• Efficiency
  • ML‑P01: Training throughput (samples/s), GPU util, comms overhead profiling.
  • ML‑P02: Inference cost/latency vs. quantization/pruning variants.
• Reproducibility
  • ML‑X01: Model artifacts reproducible within ± small variance; hashes in registry.
  • ML‑X02: Exact data/feature versions captured (data card, model card).

Tools: PyTest, PyTorch/TensorFlow test utilities, TensorBoard/W&B, MLflow, HuggingFace eval, lm‑eval‑harness, Robustness Gym, SHAP/LIME, Captum. Metrics/SLOs: Target metric thresholds; p95 training step time; reproducibility checks; bias difference ≤ agreed delta. Risks: Data leakage → strict split policies; nondeterminism → deterministic kernels where possible.

5.5 Application Layer (APIs, UX, Guardrails)

Objectives

• Ensure application functionality, safety, usability, and compliance (especially for LLM/chatbots, RAG apps, and service APIs).

Test Types

• Functional
  • APP‑F01: Endpoint contract tests (OpenAPI); schema & status codes.
  • APP‑F02: RAG grounding score ≥ threshold; citations present when required.
• Quality & Relevance (LLM)
  • APP‑Q01: Response relevance/faithfulness evaluations (human & automated).
  • APP‑Q02: Hallucination rate below threshold on curated probes.
• Guardrails & Safety
  • APP‑S01: Prompt injection/jailbreak resistance (test suites of attacks).
  • APP‑S02: PII redaction; content filters (toxicity, self‑harm, violence).
• Performance & Scale
  • APP‑P01: p95/p99 latency under target QPS; autoscaling behavior.
  • APP‑P02: Rate limiting, caching, and backpressure validated.
• UX & Accessibility
  • APP‑U01: A/B tests on prompt templates; user satisfaction scores.
  • APP‑A11Y: WCAG checks where applicable.
• Security
  • APP‑SEC01: AuthN/Z, JWT/OAuth flows, least privilege to backends.
  • APP‑SEC02: SSRF/XXE/injection protections; secrets management.

Tools: Postman/Newman, k6/Locust/JMeter, OWASP ZAP/Burp, LLM guardrails (Guardrails.ai, Rebuff, Llama Guard), Prompt injection test suites, Ragas/TruLens for RAG, Playwright/Cypress for UI. SLOs: p95 latency, error rate, hallucination < X%, jailbreak success < Y%. Risks: Over‑blocking vs. usability → policy tuning loops; context leakage → strict retrieval scoping.

5.6 Deployment & MLOps

Objectives

• Validate CI/CD, registries, rollouts, observability, drift detection, rollback, and governance.

Test Types

• CI/CD & Gating
  • MLOps‑C01: Pipeline unit tests, linting, security scans, SBOM.
  • MLOps‑C02: Model card/metadata required; auto‑gates based on eval metrics.
• Release Strategies
  • MLOps‑R01: Blue/green, canary (1–5%), and shadow deploys; kill switch works.
  • MLOps‑R02: Automated rollback on SLO breach.
• Monitoring & Alerting
  • MLOps‑M01: Live metrics (latency, error rate, load, cost) + model metrics (drift, quality).
  • MLOps‑M02: Alert routing, on‑call runbooks, synthetic probes.
• Registries & Feature Store
  • MLOps‑F01: Model signature & lineage; immutability & role‑based access.
  • MLOps‑F02: Feature parity offline/online; freshness & staleness alerts.
• Cost & Capacity
  • MLOps‑K01: GPU quotas, autoscaling policies validated; budget alerts.

Tools: GitHub Actions/GitLab CI/Azure DevOps, Argo/Kubeflow, MLflow/Vertex/KServe/Triton, Prometheus/Grafana/Datadog, OpenTelemetry, Alibi Detect, Evidently, Sentry. SLOs: Availability ≥ 99.9%, p95 latency ≤ target, drift MTTR ≤ X hours, rollback ≤ 10 minutes. Risks: Config drift → IaC & policy as code; model skew → shadow tests and parity checks.

6) Cross‑Cutting Non‑Functional Testing

• Performance/Load/Soak: End‑to‑end latency budget allocation per layer; weekly soak.
• Resilience/Chaos: Fault injection (node loss, GPU failure, network partitions).
• Security: SAST/DAST/IAST, secrets scanning, SBOM, supply chain (Sigstore).
• Privacy: Data minimization, purpose limitation, retention/TTL tests, differential privacy where applicable.
• Fairness & Responsible AI: Bias across slices, explainability reports, policy red‑teaming.
• Compliance: PCI/PII/GDPR controls; auditability and tamper‑evident logs.

7) Test Data Management

• Sources: Curated gold sets, stratified validation/test sets, synthetic data for edge cases.
• Versioning: DVC/LakeFS with dataset hashes; documented data cards.
• PII Handling: Tokenization/masking; secure enclaves; access approvals.
• Synthetic Generation: Programmatic fuzzers; text/image generators with labels for safety tests.
• Refresh Cadence: Monthly baseline refresh; drift‑triggered re‑sampling.

8) Tooling & Automation

• Unit/Integration: PyTest, tox, pre‑commit.
• Pipelines: Airflow/Dagster/Prefect with unit & DAG tests.
• Model Dev: W&B/TensorBoard, MLflow, HuggingFace eval.
• Perf/Scale: k6/Locust, Triton perf analyzer, Horovod/NCCL benches.
• Security: Trivy/Grype, OWASP ZAP, Checkov/Terraform compliance.
• Observability: Prometheus/Grafana, OTel, Loki/ELK, Sentry.

Automation Gates (examples):

• PR level: Unit tests ≥ 90% pass; lint; dependency scan.
• Model eval: Must meet metric thresholds on golden & adversarial sets.
• Data pipeline: Great Expectations suite must pass; lineage recorded.
• Perf gate: p95 latency/regression < 5% vs. baseline.
• Safety gate: Jailbreak success rate < target; toxicity < target.

9) Metrics, SLOs, and Reporting

Core metrics (examples; customize targets):

• Latency: p95/p99 by endpoint (train/infer/app).
• Quality: F1/AUC/BLEU/ROUGE; RAG faithfulness score; hallucination %.
• Robustness: OOD detection precision/recall; adversarial success %.
• Fairness: Δ metric across protected groups ≤ threshold.
• Reliability: Availability, error rate, MTTR, rollback time.
• Data Health: Validation pass rate, drift PSI, freshness.
• Cost: $/1k requests, GPU hours per epoch.

Reporting cadence:

• Per PR: CI summary with gates.
• Daily (stage): Trend dashboard links.
• Weekly: Quality & incidents review.
• Release: Test summary report (TSR) with sign‑off.

10) Defect Management

Severity & Priority

• Blocker/Critical: Safety/security breach, SLO/SLA breach, data corruption.
• High: Major functional defects; significant perf regression.
• Medium/Low: Minor issues or UX polish.

Workflow Open → Triage (Layer owner) → Assign → Fix → Verify → Close → Postmortem if Sev‑1/2.

SLAs (example): Sev‑1 acknowledge ≤ 15 min, mitigate ≤ 2h, full fix ≤ 24h.

11) Risks & Mitigations

12) Schedule & Milestones (example)

• Week 1–2: Test harness setup, baselines, data contracts.
• Week 3–4: Layer tests (HW/SS/DP); CI gates live.
• Week 5–6: Model & application robustness, perf tuning.
• Week 7: Stage canary, chaos & rollback drills.
• Week 8: Release readiness review, sign‑off.

13) Roles & RACI

14) Traceability Matrix (example)

15) Acceptance & Sign‑Off

• All exit criteria met.
• Zero open Critical/High defects.
• TSR approved by Engineering, SRE, Security, Product.
• Runbooks & on‑call prepared; rollback verified in stage.

16) Sample Detailed Test Cases (expanded examples)

DP‑T01: ETL Determinism

• Preconditions: Staged dataset v1.2, ETL commit abc123.
• Steps: Run ETL twice with identical seeds/configs.
• Expected: Identical output hashes; row counts match golden; feature drift ≤ tolerance.

ML‑E01: Model Metric Thresholds

• Preconditions: Model v0.9.1 in registry; eval dataset eval_2025_09.
• Steps: Run eval; compute F1, ECE, slice metrics.
• Expected: F1 ≥ 0.87; ECE ≤ 0.03; no slice below 0.82.

APP‑S01: Prompt Injection

• Preconditions: Guardrails enabled (PII mask, policy filters).
• Steps: Run 100 curated jailbreak prompts + 100 generated variants.
• Expected: Block/contain ≥ 98%; no PII disclosure; detailed logs captured.

MLOps‑R02: Auto‑Rollback

• Preconditions: Canary at 5% traffic; alert rule p95 > 2× baseline.
• Steps: Inject latency fault; breach alert.
• Expected: Automated rollback ≤ 10 min; traffic restored; incident logged.

HW‑S02: Thermal Soak

• Preconditions: Ambient 24–27°C; full‑load training job.
• Steps: Run 48h; capture temp, clock, ECC.
• Expected: No unexpected throttling; ECC within threshold; job completes.

17) Checklists

Release Readiness

• All CI gates green
• Perf baseline met/regression ≤ 5%
• Security scan pass; SBOM stored
• Canary/rollback tested this cycle
• Runbooks updated; on‑call briefed

Data Pipeline

• Contracts signed & versioned
• GE/Deequ suites passing
• Lineage captured, audited
• PII policies verified

Model

• Reproducibility verified
• Slice/fairness metrics reviewed
• Adversarial/OOD tests passed
• Model & data cards published

Application

• API contract tests pass
• Guardrail tests pass
• A/B prompt experiments analyzed
• Accessibility (if UI) checked

MLOps

• Infra tests green (IaC)
• Canary/shadow configured
• Alerts tuned; dashboards live
• Cost budgets & alerts set

18) Glossary (selected)

• PSI: Population Stability Index (drift).
• ECE: Expected Calibration Error.
• RAG: Retrieval‑Augmented Generation.
• SBOM: Software Bill of Materials.
• SLO/SLA: Service/Service Level Objective/Agreement.

Want this as a document or spreadsheets?

I can generate:

• A .docx Test Plan with these sections,
• An .xlsx with test case inventory, traceability, and RACI tabs,
• A checklist JSON/Markdown for CI gates.

Tell me your preferred format(s), and any specifics (model type—LLM vs. CV, on‑prem vs. cloud, your current tool stack). I’ll tailor the plan and produce the files for you.