Risk Based Testing

<objective>
Equal coverage across all features wastes effort on low-risk areas while leaving critical paths under-tested — a 90% coverage target on a settings page is effort stolen from checkout. This skill discovers risk, quantifies it as impact × probability, maps test density to risk zones, and keeps the assessment current as the product evolves. Output is a scored risk matrix that feeds `test-strategy` and `test-planning`.
</objective>

Quick Route

Discovery Questions

Check .agents/qa-project-context.md first — if it exists, use it as the foundation and skip questions already answered there. Gather the rest from stakeholders across engineering, product, and operations.

Revenue-Critical Flows

• Which user flows directly generate revenue? (checkout, subscription, billing, upgrades)
• What is the revenue impact per hour of downtime for each flow?
• Are there time-sensitive flows? (flash sales, market-hours trading, payroll deadlines)
• Which flows have contractual SLAs with financial penalties?

Recent Failures

• What broke in the last 3 releases? What escaped to production?
• What were the root causes? (code defect, config error, third-party failure, data migration)
• What was the blast radius of each incident? (users affected, revenue lost, reputation impact)
• Were there near-misses caught late in testing that could have escaped?

Fragile Areas

• Which parts of the codebase change most frequently? (high churn = high risk)
• Which modules have the lowest test coverage today?
• Which areas have the most complex business logic or the most conditional branches?
• Which code was written by engineers who have since left the team?

Third-Party Dependencies

• Which external services does the product depend on? (payment processors, auth providers, CDNs, APIs)
• What is the historical reliability of each dependency?
• What happens when each dependency goes down? (graceful degradation or hard failure?)
• Are there single points of failure with no fallback?

Compliance and Data

• What regulatory requirements apply? (GDPR, PCI-DSS, HIPAA, SOC2, SOX, EU AI Act)
• What data is most sensitive? (PII, financial, health, credentials)
• What are the legal consequences of a data breach or compliance violation?
• Are there audit requirements that mandate specific testing evidence?

Core Principles

1. Not all features are equal. A bug in checkout that blocks purchases is categorically different from a misaligned icon on a settings page. Equal coverage everywhere wastes resources on low-risk areas while leaving critical paths under-tested.
2. Risk = Impact × Probability. Risk is not a gut feeling. It is a product of two dimensions scored independently: how bad if this fails (impact), and how likely to fail (probability). Score both consistently across the product, then multiply.
3. Risk assessment is continuous. A risk model created once and never updated creates false confidence. Risk changes when the product changes, when dependencies change, when the team changes, and after every production incident. Build reassessment into the rhythm.
4. Near-misses are data. A catastrophic bug caught in staging is not a success story — it is a signal that the model underestimated that area. Track near-misses with the same rigor as production incidents.
5. Risk informs coverage, not the other way around. Do not start with "we need 80% coverage everywhere." Start with "where would a failure hurt most?" and let the model drive coverage targets per module.

Workflow

1. Identify → 2. Classify → 3. Analyze → 4. Heatmap → 5. Coverage → 6. Reassess → (repeat)
                                 ↑
                       score ≥ 10 only; skip to 4 if no items reach threshold

Phase 1: Risk Identification

Enumerate everything that could go wrong. Cast a wide net. Sources include:

• Stakeholder interviews: Product managers know business-critical flows. Engineers know fragile code. Support knows recurring user complaints.
• Incident history: Past failures predict future failures. Review post-mortems from the last 6–12 months.
• Dependency mapping: List every external service, database, message queue, and third-party API. Each is a risk vector.
• Change analysis: Areas with frequent code changes have higher defect probability. Use the ranked churn command in Phase 6 to find them; git log --stat <file> is for per-commit inspection of a specific suspect, not for ranking.
• Architecture review: Shared databases, single points of failure, synchronous chains, and tightly coupled modules amplify blast radius.

Use HTSM v6.3 (Heuristic Test Strategy Model, Bach) as a Phase-1 lens — its state-based and boundary heuristics surface risks a pure feature-list misses. Download: https://www.satisfice.com/download/heuristic-test-strategy-model

Output: a raw list of risk items, each describing what could fail and what the consequence would be.

Phase 2: Risk Classification

Categorize each risk item along two axes.

Impact categories (how bad is it):

Probability categories (how likely is it):

Composite score = Impact × Probability. Frequent changes indicate defect probability, so a Moderate-impact (3) feature under heavy churn scores Probability 5 → Risk score: 15 → CRITICAL zone, despite "only" moderate impact. The composite score drives priority, not impact alone.

Phase 3: Failure Mode Analysis

For each high-risk item (score ≥ 10), perform a detailed failure mode analysis.

Feature/Component: [name]
Risk Score: [impact × probability]

Failure Mode 1: [what specifically can fail]
  Trigger:            [what causes this failure]
  Blast Radius:       [users affected, systems affected, data affected]
  Detection Method:   [how would we know -- monitoring, user report, test]
  Current Mitigation: [existing tests, monitoring, feature flags, fallbacks]
  Gap:                [what is missing from current mitigation]

Failure Mode 2: ...

Example — E-commerce Checkout (Risk Score 20, Impact 5 × Probability 4):

Failure Mode 1: Payment charge succeeds but order not recorded
  Trigger:            Race condition between payment API callback and order write
  Blast Radius:       Individual users; money charged but no order confirmation
  Detection Method:   Payment reconciliation job (runs hourly), user complaint
  Current Mitigation: Idempotency key on payment, retry on order write
  Gap:                No automated test for the race condition; reconciliation delay is 1 hour

Failure Mode 2: Discount code applies incorrect amount
  Trigger:            Percentage discount on already-discounted item
  Blast Radius:       All users with stacked discounts; revenue leakage
  Detection Method:   Margin monitoring alert (>5% deviation)
  Current Mitigation: Unit tests for single discounts
  Gap:                No tests for discount stacking; no tests for rounding edge cases

Failure Mode 3: Inventory not reserved during checkout
  Trigger:            Concurrent purchases of last-stock item
  Blast Radius:       Oversold items, fulfillment failure, customer trust
  Detection Method:   Fulfillment team discovers during packing
  Current Mitigation: Database-level stock check on order creation
  Gap:                No load test simulating concurrent last-item purchases

AI/LLM failure classes

For AI/LLM features, classify against these CT-GenAI classes and score Impact and Probability independently like any other risk. The mitigation is the existence of an automated eval suite, not a single manual test.

AI/LLM-specific failure classes (from ISTQB CT-GenAI v1.1, effective 27 April 2026):

• Hallucination / reasoning error — Impact: moderate to major; Probability: high without explicit prompt-eval coverage. Detection: golden-dataset evals, fact-check assertions (see ai-system-testing).
• Bias — Impact: catastrophic in regulated industries (finance, healthcare, hiring). Probability: dataset-dependent. Detection: counterfactual evals, demographic-parity checks.
• Prompt injection / jailbreak — Impact: major (data exfiltration, prompt extraction). Probability: high for any externally-facing LLM feature. Detection: Garak, PyRIT, Promptfoo redteam.
• Privacy leak — Impact: catastrophic under GDPR/CCPA/EU AI Act. Probability: dataset-dependent. Detection: PII scanning of training data and prompts.
• AI Act / regulatory non-compliance — Impact: catastrophic (fines, ban). Probability: high for EU-facing AI features. Detection: see compliance-testing.

Tool freshness (mid-2026): PyRIT now lives at microsoft/PyRIT — the old Azure-hosted repo was archived March 2026, so do not point new redteam work at the legacy Azure path. Promptfoo was acquired by OpenAI (March 2026) but remains MIT-licensed. Garak is current and unchanged.

Reference frameworks: CT-GenAI v1.1 (ISTQB, effective 27 April 2026) codifies the AI/LLM classes above. WQR 2025-26 (Capgemini, 17th edition, Nov 2025) gives the adoption-stage framing for AI risk planning.

Phase 4: Risk Heatmap

Plot all risk items on a 5×5 matrix to communicate priorities and drive coverage decisions.

                    PROBABILITY
                    Rare(1)   Unlikely(2)  Possible(3)  Likely(4)   Frequent(5)
                   +----------+-----------+-----------+----------+-----------+
  Catastrophic(5)  |  5  MED  | 10  HIGH  | 15  CRIT  | 20 CRIT  | 25  CRIT  |
                   +----------+-----------+-----------+----------+-----------+
  Major(4)         |  4  LOW  |  8  MED   | 12  HIGH  | 16 CRIT  | 20  CRIT  |
I                  +----------+-----------+-----------+----------+-----------+
M  Moderate(3)     |  3  LOW  |  6  MED   |  9  MED   | 12 HIGH  | 15  CRIT  |
P                  +----------+-----------+-----------+----------+-----------+
A  Minor(2)        |  2  LOW  |  4  LOW   |  6  MED   |  8 MED   | 10  HIGH  |
C                  +----------+-----------+-----------+----------+-----------+
T  Negligible(1)   |  1  LOW  |  2  LOW   |  3  LOW   |  4 LOW   |  5  MED   |
                   +----------+-----------+-----------+----------+-----------+

Zone boundaries and action mapping:

Populated example (where each named risk lands):

                    Rare(1)   Unlikely(2)  Possible(3)  Likely(4)   Frequent(5)
  Catastrophic(5)             Auth bypass   Payments fail  Checkout crash
  Major(4)                                  Data export    Search broken  User upload
  Moderate(3)                  Report fmt    Email deliver  Profile edit
  Minor(2)         Footer link Tooltip text  Theme switch
  Negligible(1)    Admin label

Phase 5: Test Coverage Alignment

Map test density to risk level. Every zone gets a prescribed approach.