🧪 QA Engineer Agent Specification¶

🎯 Purpose¶

The QA Engineer Agent is the central quality coordinator in the ConnectSoft AI Software Factory. Its purpose is to:

Ensure software outputs (services, modules, apps, screens, APIs) meet functional, behavioral, and non-functional quality requirements
Validate build readiness for CI/CD pipelines across editions, platforms, and tenant configurations
Serve as the glue layer between Test Generators, Automation Agents, Observability systems, and Studio QA dashboards
Enforce a "Quality Gate" mindset before promotion or release, autonomously but traceably

It transforms test data, coverage metrics, runtime telemetry, and change diffs into structured QA intelligence.

🧭 Strategic Role in ConnectSoft AI Software Factory¶

The QA Engineer Agent is invoked post-test execution and pre-release decisioning. It consolidates results from:

✅ Test Automation Engineer Agent
✅ Test Case Generator Agent
✅ Load & Performance Testing Agent
✅ Resiliency & Chaos Engineer Agent
✅ Bug Investigator Agent
✅ Observability Agent
✅ Code Reviewer Agent

It scores, flags, or approves the build’s test readiness.

🔁 Agent Placement in QA Flow¶

flowchart TD
    TestGen[Test Generator Agent]
    Auto[Test Automation Agent]
    Perf[Load/Performance Agent]
    Chaos[Chaos Engineer Agent]
    Bug[Bug Investigator]
    QA[[🧪 QA Engineer Agent]]
    Studio[Studio Dashboard]
    CI[CI/CD Agent]

    TestGen --> Auto --> QA
    Perf --> QA
    Chaos --> QA
    Bug --> QA
    QA --> Studio
    QA --> CI

Hold "Alt" / "Option" to enable pan & zoom

🎯 What the QA Engineer Agent Guarantees¶

Guarantee	Description
Test Readiness Status	Every build has a scored QA quality report with regression and coverage metrics
Edition-Specific QA Validation	Verifies that all enabled features are tested in the right context (B2B/B2C, locale, branding)
Functional Test Gap Detection	Flags missing test coverage per module, screen, or API
Observability-Aware Analysis	Uses traces/logs to augment test validation (e.g., crashed screen not covered by test suite)
Human-Aware Gatekeeping	Routes critical decisions to HumanOps when policy or confidence thresholds are breached
Build Confidence Index	Scores every build with pass/fail % + risk level (e.g., `confidenceScore: 0.87`, `status: requires review`)

🧱 Quality Philosophy¶

The agent is guided by the principle:

“Every output must be testably valid, observably safe, and regressively stable — across all editions, tenants, and platforms.”

This ensures ConnectSoft outputs are defensible, maintainable, and release-ready at scale.

🔐 Compliance & Non-Functional Scope¶

While direct testing is performed by other agents, the QA Engineer Agent enforces:

Test plan completeness
Negative testing coverage
Privacy-aware test flags (e.g., GDPR erasure)
Accessibility validation status
Edition-specific toggles and edge flows

🧑‍💻 HumanOps Role¶

The agent does not write tests, but may:

Reject or flag builds
Escalate edge cases
Emit qa-review.md when coverage or confidence is below policy

✅ Summary¶

The QA Engineer Agent:

🎯 Orchestrates post-test build QA judgment
📊 Consolidates test, telemetry, coverage, and change inputs
🔎 Identifies gaps, regressions, or unstable flows
🟢 Outputs pass/fail + confidence score + Studio metadata

It is the final authority on software test quality, ensuring only QA-validated code ships — in a multi-agent, multi-tenant, and AI-first delivery pipeline.

📋 Core Responsibilities¶

The QA Engineer Agent owns the post-execution validation layer within ConnectSoft's AI Software Factory. While other agents execute or generate tests, the QA Engineer Agent is responsible for asserting release safety, identifying regressions, and scoring build confidence.

Its role is horizontal across all delivery surfaces — backend, frontend, mobile, API, edition, and tenant.

🧭 Primary Responsibilities¶

Category	Responsibility
✅ Build QA Status Evaluation	Aggregate all test results, telemetry, trace evidence, and coverage reports to decide if a build is “release-safe.”
📊 Test Coverage Scoring	Compute and store module-level and global test coverage (unit, integration, UI, E2E, chaos).
🔁 Regression & Drift Analysis	Detects behavior divergence between test runs, missing regression assertions, or test gaps on changed areas.
🧩 Edition & Tenant QA Enforcement	Ensure edition-specific logic is test-covered (e.g., onboarding screens, themes, region toggles).
🔬 Negative Path & Edge Flow Check	Audit test suites for absence of error, boundary, or invalid input paths.
🧠 Test Intelligence from Observability	Detect untested crashes, 404s, or API errors based on traces/logs (even if test passed).
🛑 Test Gate Enforcement	Blocks or flags builds based on confidence threshold and policy configuration.
📤 Studio Dashboard Reporting	Emit QA matrices, screen coverage heatmaps, build status artifacts, and action items for other agents or humans.
🧑‍💻 Human Review Routing	Trigger `qa-review.md` or `ManualQAGateRequired` event when score < threshold or policy is ambiguous.

🗂️ Reported Outputs (Preview)¶

Output File	Description
`qa-summary.json`	Final score, metrics, pass/fail flags, coverage %
`qa-overview.md`	Human-readable summary: coverage, risks, regressions, edition compliance
`regression-matrix.json`	What changed, what failed, what was missed
`test-gap-report.yaml`	Screens, services, or flows with missing coverage
`studio.qa.build.*`	Exports used by Studio dashboards (modules, trace IDs, edition tags, tenant tags)

📘 Sample QA Output Snippet¶

{
  "buildId": "connectsoft-mobile-v4",
  "status": "requires-review",
  "confidenceScore": 0.82,
  "testsExecuted": 1374,
  "testsPassed": 1350,
  "coverage": {
    "unit": 81.5,
    "integration": 74.2,
    "e2e": 62.0
  },
  "regressionsDetected": 2,
  "untestedChanges": 7
}

🚦 Validation Scope Types¶

Scope	Enforced
Screen flow validation	✅
API response assertions	✅
Auth + session handling	✅
Edition behavior toggles	✅
Multitenant separation (via test config)	✅
Visual diff or UX regressions	❌ (handled by UI Visual Diff Agent, if added later)

🧑‍💻 Developer-Centric Interactions¶

Build result comment for PRs
Markdown QA summary injected into GitHub/GitLab/Azure DevOps
Warnings presented visually in Studio CI tab or trace-linked dashboard

✅ Summary¶

The QA Engineer Agent:

🛠️ Aggregates and evaluates all test evidence
🔬 Detects missing or ineffective test coverage
🔁 Performs regression and drift analysis
🧾 Outputs trace-linked QA metadata
🟠 Triggers manual review if confidence or scope is unclear

It is the final autonomous authority on test quality in every build before release or Studio publish.

📥 Inputs Consumed¶

The QA Engineer Agent consolidates a wide spectrum of structured artifacts from other agents, observability tools, and CI pipelines. These inputs allow it to form a complete picture of software quality, contextualized by platform, edition, and tenant.

🧩 Structured Inputs by Source¶

Input	Provided By	Description
`test-results.json`	Test Automation Engineer Agent	Aggregated test execution report with pass/fail, duration, category
`coverage-summary.json`	Test Generator or CI Agent	Coverage percentages per file, screen, endpoint, and test type
`regression-index.yaml`	Bug Investigator or QA memory	Previously known issues, test regressions, fixed-but-unverified areas
`trace-logs.json`	Observability Agent	OpenTelemetry span summaries, 500s, crashes, user behavior not covered by tests
`build-manifest.json`	CI/CD Agent	Version, commit hash, change delta, modules affected, build variant
`edition-config.yaml`	Edition Coordinator Agent	Branding-specific feature toggles, routes, themes, screens that must be tested
`manual-test-tags.yaml`	HumanOps Agent or QA Manager	Known areas requiring manual coverage, or exception areas (e.g., complex UI, animations)
`qa-policy.yaml`	Orchestrator or Factory Ops	Rules for confidence thresholds, fail/pass logic, edition-specific exceptions
`studio-annotations.json`	Studio	Annotations, known bugs, UX feedback, previously accepted coverage gaps

📘 Sample: `coverage-summary.json`¶

{
  "unit": 81.5,
  "integration": 75.3,
  "e2e": 63.0,
  "screens": {
    "LoginScreen": { "unit": 100, "e2e": 80 },
    "DashboardScreen": { "unit": 85, "e2e": 40 }
  },
  "apis": {
    "/appointments": { "tested": true },
    "/notifications": { "tested": false }
  }
}

🧠 Semantic Inputs (via SK Prompt / Memory)¶

Semantic Input	Example
`changedSinceLastRun`	`['appointmentsService', 'notificationsScreen']`
`regressionSuspectedIn`	`['OnboardingCarousel', 'EmailVerificationFlow']`
`lastBuildConfidenceScore`	`0.92`
`manualReviewRequired`	`false`
`strictEditionQAEnabled`	`true`

🧪 Test Type Classification¶

Test Type	Artifact
✅ Unit	`unit-test-results.json`
✅ Integration	`integration-test-results.json`
✅ UI / Widget	`ui-test-map.json`
✅ E2E	`bdd-results.json`, `studio-e2e.yaml`
✅ Chaos	`chaos-impact-report.json`
✅ Load/Perf	`performance-metrics.json`
⬜ Visual	(Planned for future agents)

🔎 QA Policy Input (`qa-policy.yaml`)¶

minConfidenceScore: 0.85
minE2ECoverage: 60
requireEditionCoverage: true
blockOnRegression: true
allowedManualBypass: false

→ Agent uses this to decide: approve, block, or escalate.

🌍 Edition-Specific Overrides¶

Agent loads per-edition test exceptions or feature requirements from:

edition-test-map.yaml
tenant-test-config.yaml
manual-test-tags.yaml

Example:

edition: vetclinic-blue
excludedScreens: [MarketingConsentScreen]
requiredScreens: [Onboarding, LoginScreen]

✅ Summary¶

The QA Engineer Agent consumes:

📄 Test result files
🔬 Coverage summaries
🔁 Regression indices
📈 Observability logs
📋 QA policy and configuration
🎨 Edition-level test overlays

These inputs allow it to reason holistically about build health, regressions, and test effectiveness.

📤 Outputs Produced¶

The QA Engineer Agent emits a complete QA intelligence bundle that informs:

🛑 CI/CD release gates
📊 Studio dashboards
🧑‍💻 Human review workflows
🔁 Test planning for regressions and gaps
📁 QA artifact archives for traceability

These outputs are structured, versioned, and trace-linked to specific builds, tenants, and editions.

📁 Primary Output Artifacts¶

File	Description
`qa-summary.json`	Structured QA verdict: pass/fail, score, metrics, traceId
`qa-overview.md`	Markdown report: human-readable QA summary for Studio/PR
`regression-matrix.json`	Comparison of current vs previous runs; shows new, repeated, and fixed failures
`test-gap-report.yaml`	Maps missing test coverage by module, screen, API, or flow
`build-confidence.json`	Final confidence score with breakdown (unit, UI, E2E, chaos, observability)
`studio.qa.status.json`	Export to feed Studio dashboards for QA status badges, heatmaps, and analytics
`manual-review-needed.md`	If score < threshold or config requires human override
`qa-trace-index.json`	Contains traceId, tenantId, editionId, platform, build version

📘 Sample: `qa-summary.json`¶

{
  "traceId": "proj-811-v2",
  "buildId": "bookingapp-v5.2.0",
  "status": "pass",
  "confidenceScore": 0.91,
  "tests": {
    "executed": 1438,
    "passed": 1431,
    "failed": 7
  },
  "coverage": {
    "unit": 83.4,
    "integration": 77.9,
    "e2e": 65.2,
    "chaos": "partial"
  },
  "regressions": 0,
  "manualReview": false
}

📘 Sample: `qa-overview.md`¶

# QA Overview — Build bookingapp-v5.2.0

**Status**: ✅ Passed  
**Confidence Score**: 91.0%  
**Tests Executed**: 1438 (7 failed)  
**Coverage**:  
- Unit: 83.4%  
- Integration: 77.9%  
- E2E: 65.2%  
- Chaos: Partial  

**Regressions**: None  
**Untested Changes**: 2 modules (notificationsService, FeedbackScreen)

_No manual review required. Safe to proceed to release._

> QA Engineer Agent • Edition: vetclinic-blue • Trace: proj-811-v2

📘 Sample: `test-gap-report.yaml`¶

untestedModules:
  - notificationsService
  - subscriptionHelper
screensWithNoE2E:
  - FeedbackScreen
  - DeleteAccountScreen
missingNegativePaths:
  - LoginScreen (no 401 tested)
  - PaymentFailureFlow

📊 Output Tags and Traceability¶

All outputs include:

traceId
tenantId, editionId
platform (flutter, maui, react-native)
buildId, version, buildTimestamp
sourceBranch, commitSha

These tags ensure Studio dashboards and Orchestrator flows remain audit-ready and artifact-linked.

🚦 CI/CD Output Behavior¶

Result	Action
`status: pass`	Mark build green, allow deploy
`status: requires-review`	Halt CI, post comment in PR
`status: fail`	Block pipeline, notify HumanOps Agent

📤 Studio/Orchestrator Integration¶

Output File	Consumed By
`studio.qa.status.json`	Studio dashboards
`qa-summary.json`	Orchestrator + DevOps Agent
`manual-review-needed.md`	HumanOps Agent
`regression-matrix.json`	Bug Investigator Agent
`test-gap-report.yaml`	Test Generator + Automation Agents

✅ Summary¶

The QA Engineer Agent produces:

📊 Machine-readable verdicts
📄 Human-friendly Markdown summaries
🔁 Regression + test gap analysis
📎 Trace-tagged, edition-aware outputs
🎯 Studio and CI/CD-compatible QA artifacts

These outputs act as the final quality checkpoint before any module, microservice, or mobile app proceeds to release or tenant deployment.

🔄 Execution Flow¶

The QA Engineer Agent follows a deterministic multi-phase process to analyze test evidence, verify build stability, and emit a confidence-scored QA verdict. The flow integrates test execution artifacts, observability insights, edition rules, and prior regressions.

🔁 High-Level Execution Pipeline¶

flowchart TD
    START[🚀 Start QA Agent Session]
    LOAD[📥 Load Inputs (results, coverage, traces)]
    POLICY[📄 Load QA Policy & Edition Config]
    ANALYZE[🧠 Analyze Tests, Coverage, Observability]
    SCORE[📊 Compute Confidence Score]
    REGRESS[🔁 Check for Regressions & Test Drift]
    VERIFY[🔎 Verify Edition-Specific QA]
    GATE{✅ Pass Threshold?}
    REPORT[📤 Generate QA Reports]
    ESCALATE[🧑‍💻 Emit Manual Review Trigger]
    DONE[🏁 Emit Studio + CI/CD Outputs]

    START --> LOAD --> POLICY --> ANALYZE --> SCORE --> REGRESS --> VERIFY --> GATE
    GATE -- Yes --> REPORT --> DONE
    GATE -- No --> ESCALATE --> DONE

Hold "Alt" / "Option" to enable pan & zoom

🪜 Execution Phase Breakdown¶

Phase	Description
1. Load Inputs	Ingests `test-results.json`, `coverage-summary.json`, `trace-logs.json`, `edition-config.yaml`, `qa-policy.yaml`
2. Apply QA Policy	Reads policy for min confidence, edition enforcement, allowed manual overrides
3. Analyze Results	Computes pass/fail %, coverage % per type, missing test cases
4. Score Build	Calculates final confidence score (e.g., `0.91`), explains score factors
5. Regression Detection	Compares with prior run’s matrix: fixed, repeated, new regressions
6. Edition QA Check	Ensures all edition-specific routes, features, and flows were covered
7. Decision Gate	Compares confidence score and regression flags with policy to decide outcome
8. Output Generation	Produces all reports and summary artifacts, updates Studio + CI/CD
9. Escalation	If score fails policy or coverage is insufficient → trigger HumanOps & QA review

📘 Example: Build Confidence Calculation¶

Factor	Value	Weight	Score
Test pass rate	99.5%	0.4	0.398
Unit test coverage	85%	0.2	0.170
Integration test coverage	75%	0.1	0.075
E2E coverage	62%	0.15	0.093
No regressions	✅	0.1	0.10
Observability drift	None	0.05	0.05

Final Score: 0.886 → QA Pass ✅

🧑‍💻 Escalation Flow (if needed)¶

If any of the following occurs:

confidenceScore < minConfidenceScore
criticalRegressionsDetected = true
missingEditionFlowTests = true
chaosTestFailed = true

Then:

Emit manual-review-needed.md
Set status: requires-review
Notify Studio, HumanOps Agent, and QA Manager

📋 Execution Metadata Output¶

{
  "traceId": "proj-811-v2",
  "buildId": "booking-v5.2.0",
  "status": "pass",
  "confidenceScore": 0.886,
  "executionCompletedAt": "2025-05-15T22:08:00Z",
  "regressionsFound": 0,
  "manualReviewTriggered": false
}

🧠 Determinism & Repeatability¶

Execution is idempotent per input bundle
All outputs are trace-tagged and reproducible
Agent may cache coverage diffs to optimize multi-module pipelines

✅ Summary¶

The QA Engineer Agent:

🧠 Analyzes multi-agent inputs
📊 Scores build quality
🔁 Detects regressions and gaps
🛑 Enforces pass/fail policy gates
🧾 Emits Studio/CI/CD outputs
🧑‍💻 Escalates only when policy demands human review

Its flow is structured, traceable, and CI-native — enabling continuous, agent-driven QA enforcement across editions and platforms.

🧩 Skills and Semantic Kernel Functions¶

The QA Engineer Agent is powered by a modular set of Semantic Kernel (SK) skills, each aligned with a specific validation task in the QA lifecycle. These skills transform structured test artifacts and runtime traces into a final QA verdict, regression insight, and coverage intelligence.

🧠 Core Semantic Kernel Skills¶

Skill Name	Role
`ValidateBuildQualitySkill`	Central orchestrator: loads inputs, invokes other skills, produces verdict
`ComputeConfidenceScoreSkill`	Applies weighted QA policy to test coverage, pass rate, regressions
`AnalyzeCoverageSkill`	Detects untested modules, missing screens, coverage holes
`DetectRegressionSkill`	Compares previous vs current run to identify regressions and test drift
`VerifyEditionCoverageSkill`	Ensures branding-specific flows, routes, and screens are tested
`AnalyzeObservabilitySkill`	Uses OpenTelemetry/logs to identify missed runtime issues (e.g., crashes not seen in tests)
`GenerateQAReportsSkill`	Emits `qa-summary.json`, `qa-overview.md`, `test-gap-report.yaml`
`EmitStudioQaStatusSkill`	Creates Studio-compatible QA trace exports and status for dashboards
`EscalateManualReviewSkill`	Triggered if score is below policy or manual QA is configured
`TagOutputWithTraceSkill`	Ensures all outputs have `traceId`, `tenantId`, `editionId`, `platform` for auditing

📘 Sample Skill Call – `ComputeConfidenceScoreSkill`¶

Input:

{
  "unitCoverage": 83.4,
  "integrationCoverage": 75.3,
  "e2eCoverage": 62.1,
  "testsPassed": 1382,
  "testsTotal": 1391,
  "regressions": 0,
  "observabilityWarnings": false
}

Output:

{
  "confidenceScore": 0.91,
  "status": "pass"
}

📎 Trace Metadata Injected by Skills¶

Every skill execution attaches:

traceId
buildId
skillName
executionTimestamp
tenantId, editionId
platformTarget (e.g., flutter, maui, react-native)
confidenceScoreBefore, confidenceScoreAfter (if iterative)

🔁 Skill Reuse Across Agents¶

Shared Skill	Used By
`AnalyzeObservabilitySkill`	QA Engineer Agent, Bug Investigator Agent
`GenerateQAReportsSkill`	QA Agent, Studio Agent
`DetectRegressionSkill`	QA Agent, Retry Agent, Bug Investigator
`TagOutputWithTraceSkill`	All Engineering + QA Agents

🛠️ Skill Customization Based on Policy¶

Policies passed to ValidateBuildQualitySkill control skill behaviors:

qaPolicy:
  minConfidenceScore: 0.85
  requireE2E: true
  failOnRegression: true
  allowManualOverride: false

→ Affects scoring thresholds, whether to fail or route to EscalateManualReviewSkill.

✅ Summary¶

The QA Engineer Agent uses skills to:

📊 Score builds
🔎 Detect regressions
🔬 Analyze test and runtime coverage
📤 Emit dashboards + decision reports
🧑‍💻 Escalate intelligently

Its SK skill system is composable, audit-safe, policy-driven, and aligned with Clean QA boundaries in the AI Software Factory.

📈 Test Coverage Management¶

This section defines how the QA Engineer Agent evaluates and manages test coverage across all types (unit, integration, UI, E2E, chaos), platforms, modules, and editions. Coverage data is used to compute the confidence score, detect gaps, and influence release gating decisions.

🎯 Types of Coverage Tracked¶

Type	Description	Source
Unit	Method/class/function tests	`coverage-summary.json` (unit)
Integration	Service boundaries, data pipelines, external APIs	`integration-test-results.json`
UI / Widget	Component rendering, user interactions	`ui-test-map.json`, `detox`, `golden`
E2E	Full user flow, routing, cross-module testing	`bdd-results.json`, `studio-e2e.yaml`
Chaos / Resilience	Fault injection, retries, failover behavior	`chaos-impact-report.json`
Performance-Aware	Load-influenced test pass/fail thresholds	`performance-metrics.json`

📘 Sample Input: `coverage-summary.json`¶

{
  "unit": 83.4,
  "integration": 76.2,
  "e2e": 64.5,
  "ui": 78.1,
  "chaos": "partial",
  "modules": {
    "appointmentsService": {
      "unit": 92,
      "e2e": 71
    },
    "loginScreen": {
      "ui": 95,
      "e2e": 88
    }
  }
}

🧪 Coverage Threshold Rules¶

Threshold	Minimum (default)	Notes
`unitCoverage`	80%	Code-focused microservices
`integrationCoverage`	70%	System boundary expectations
`e2eCoverage`	60%	Studio-safe threshold
`uiCoverage`	75%	Required on all visible screens
`chaosCoverage`	Partial acceptable	Blocks only if critical flows fail

All thresholds are configurable via qa-policy.yaml.

📄 Example QA Policy Fragment¶

qaPolicy:
  minConfidenceScore: 0.87
  minE2ECoverage: 60
  enforceEditionFlows: true
  requireTestIdsForUI: true

📍 Coverage by Entity¶

Entity	Coverage
`screens`	E2E, UI, testIds present
`microservices`	Unit, integration
`APIs (OpenAPI)`	Each endpoint must be exercised
`features`	Per-edition or tenant flow toggles must be test-covered
`critical flows`	Login, onboarding, checkout, etc., must be 100% covered E2E

🔎 Test Coverage Gaps → `test-gap-report.yaml`¶

missingCoverage:
  - module: notificationsService
    type: integration
  - screen: FeedbackScreen
    type: e2e
  - endpoint: /cancel-appointment
    tested: false
recommendations:
  - Add integration test to cover edge case for email notifications
  - Write BDD flow for deleting account with reason

📊 Heatmap Metadata for Studio¶

Metric	Value
`screenTestedCount`	28/30
`servicesWithUnitTests`	12/14
`apiEndpointsCovered`	90.2%
`screensMissingTestId`	1
`highRiskModulesMissingTests`	0

✅ Summary¶

The QA Engineer Agent:

📊 Tracks all test types across surfaces
🧠 Associates coverage with confidence scoring
🧩 Links gaps to regressions or change impact
📄 Reports in test-gap-report.yaml, qa-summary.json, and Studio dashboards

It enforces coverage-aware QA automation aligned with ConnectSoft’s modular, edition-sensitive, and release-safe philosophy.

📋 Validation Policies & Checklists¶

This section defines the validation rules, checklists, and policy-driven conditions the QA Engineer Agent uses to assert whether a build is release-safe, coverage-complete, and regression-free. These rules are enforced across environments, editions, and tenants.

📄 QA Policy Source¶

Policies are defined in:

qa-policy.yaml — global factory config
edition-policy-overrides.yaml — per-edition QA constraints
manual-test-tags.yaml — required flows/scenarios for human execution

✅ Default QA Policy Rules¶

Rule	Description	Default
`minConfidenceScore`	Final score threshold to pass	`0.85`
`requireE2ECoverage`	E2E coverage must meet `minE2ECoverage`	`true`
`minE2ECoverage`	% of required flow tests	`60`
`failOnRegression`	Any unapproved regression blocks build	`true`
`enforceEditionFlows`	Verify all edition-specific routes/features are tested	`true`
`requireTestIdsForUI`	Screen components must have `testId` or accessibility labels	`true`
`allowManualOverride`	Allow HumanOps override for borderline failures	`false`

📘 Sample: `qa-policy.yaml`¶

qaPolicy:
  minConfidenceScore: 0.87
  requireE2ECoverage: true
  minE2ECoverage: 65
  enforceEditionFlows: true
  failOnRegression: true
  requireTestIdsForUI: true
  allowManualOverride: false

📋 Edition-Specific Checklist (from `edition-policy-overrides.yaml`)¶

edition: vetclinic-premium
requiredScreens:
  - LoginScreen
  - OnboardingCarousel
  - Appointments
mustPassTests:
  - GDPRDeletionFlow
  - EmailConsentTracking
excludedFromE2E:
  - MarketingLanding
minCoverageOverrides:
  e2e: 70

→ Used to enforce edition branding QA boundaries.

🧪 Additional Checklists Validated¶

Checklist	Validated
✅ All required screens have test coverage	`qa-summary.json`
✅ Negative test cases exist for login, payment, and delete flows	`test-gap-report.yaml`
✅ Observability spans linked to user-critical flows	`trace-logs.json`
✅ Tenant routes are protected from cross-tenant leakage	Regression + contract tests
✅ Auth & logout flow stability	Tracked over past 3 builds
✅ Chaos test results (if configured)	`chaos-impact-report.json`

📄 QA Gate Decision Heuristics¶

Condition	Outcome
`score ≥ threshold` + `no regressions` + `edition coverage OK`	✅ Auto-pass
`score ≥ threshold` + `some test warnings`	⚠️ Requires Review
`score < threshold` or `regression found`	❌ Fail, block build
`manual override allowed`	🔓 Route to HumanOps Agent

📎 Visual Display in Studio¶

Metric	Studio Tile
`Test Gap Count`	❌ if > 2 modules missing
`Screen Coverage`	✅ green if ≥ 90%
`Regression Count`	🔥 red if ≥ 1
`Confidence Score`	🔵 badge with %
`Edition QA Passed`	✅ if all edition rules satisfied

✅ Summary¶

The QA Engineer Agent:

📄 Uses declarative YAML-based policy rules
✅ Applies validation checklists per edition and build type
🧪 Scores tests, regressions, coverage, and runtime traces against policy
🛑 Blocks, passes, or escalates builds based on policy match

This guarantees compliance-aligned, edition-sensitive QA enforcement across all agent-generated software in the ConnectSoft factory.

🔁 Regression and Drift Detection¶

This section outlines how the QA Engineer Agent detects test regressions, untested changes, and behavioral drift between builds. These mechanisms are essential for ensuring release safety and catching instability even when tests appear to pass.

🔍 Types of Regressions Detected¶

Type	Description
Test Failure Regression	A previously passing test now fails
Untested Change Drift	Code/modules changed but no new tests added or re-executed
Coverage Regressions	A previously tested screen or endpoint now has reduced coverage
Runtime Behavior Drift	Span logs show new errors or behaviors not observed previously (even if tests pass)
Contract/Test Mismatch	Backend contract changed but no updated contract/integration tests detected

🧠 Regression Memory¶

Stored in:

regression-index.yaml
build-qa-history.json
Semantic memory (via vector DB or trace-linked diff cache)

This memory includes:

Last known passing test IDs
Regression signature hashes
Known flaky or false-positive results (tagged manually or by frequency)

📘 Sample: `regression-index.yaml`¶

regressions:
  - id: LoginWithWrongPassword
    lastPassedBuild: bookingapp-v5.1.1
    failedIn: bookingapp-v5.2.0
    module: authService
    impactedEdition: vetclinic-premium
drift:
  - screen: OnboardingCarousel
    status: modified
    tested: false
    recommended: rerun e2e:OnboardingFlow

🔁 Detection Algorithm¶

flowchart TD
    A[Compare build coverage + trace]
    B[Detect changed files/modules]
    C[Match to executed tests]
    D{Tests changed?}
    E[Flag as "untested change"]
    F[Cross-check failures with last passing test set]
    G{Previously passed?}
    H[Flag as regression]
    I[Log drift matrix]

    A --> B --> C --> D
    D -- No --> E
    D -- Yes --> F --> G
    G -- Yes --> H --> I
    G -- No --> I

Hold "Alt" / "Option" to enable pan & zoom

📘 Sample Drift Report (`regression-matrix.json`)¶

{
  "build": "booking-v5.2.0",
  "previousBuild": "booking-v5.1.1",
  "regressions": [
    "LoginWithWrongPassword",
    "TokenExpiryAutoLogout"
  ],
  "untestedChanges": [
    "FeedbackScreen",
    "notificationsService"
  ],
  "coverageRegression": [
    "DeleteAccountFlow"
  ]
}

🎯 Outputs Affected¶

Reduces confidenceScore
Triggers manual-review-needed.md
Blocks CI/CD (if policy says failOnRegression: true)
Adds regressionCount to qa-summary.json

📘 `qa-summary.json` with Regression Flag¶

{
  "confidenceScore": 0.84,
  "status": "requires-review",
  "regressions": 2,
  "untestedChanges": 3
}

🛑 Studio Display¶

Widget	Condition	Result
🚨 Regression Count	> 0	Red badge + CI block
🧠 Drift Count	> 2 modules	Warning with rerun suggestion
🔄 Coverage Delta	-5% since last build	Requires test rewrite flag

✅ Summary¶

The QA Engineer Agent:

🔁 Detects regressions by diffing builds, traces, and test outcomes
📊 Tracks drift from untested or reduced-coverage areas
🧠 Uses memory and trace links to avoid false positives
🔥 Escalates regressions to fail builds or rerun test modules

Regression detection is central to risk-aware automation in ConnectSoft’s AI-generated code pipelines.

🧑‍💻 Human-Aware Escalation Points¶

This section defines how the QA Engineer Agent detects situations requiring human intervention and provides structured artifacts to guide manual QA decisions when automation confidence is insufficient.

Escalation is policy-driven and trace-linked, ensuring developers, QA managers, or HumanOps agents can make informed go/no-go decisions.

🎯 Escalation Triggers¶

Trigger	Condition
❌ Confidence score below threshold	`confidenceScore < minConfidenceScore` from `qa-policy.yaml`
🔁 Unapproved regressions	New failing tests previously marked stable
🧪 Untested drift on critical flows	Changed screens/modules without test coverage
🎨 Edition-specific validation skipped or failed	Required screen/tests per edition not validated
🔒 Observability-triggered issues	Runtime span or log errors not covered by tests
🧩 Missing manual test areas	`manual-test-tags.yaml` includes flows not tested

📘 Escalation Output: `manual-review-needed.md`¶

# Manual QA Review Required

**Build:** booking-v5.2.0  
**Trace ID:** proj-811-v2  
**Status:** ⚠️ Requires Review  
**Confidence Score:** 0.82  
**Regressions:** 2  
**Untested Changes:** FeedbackScreen, subscriptionHelper

---

## Required Review Areas

- LoginWithWrongPassword — now failing
- TokenExpiryAutoLogout — crash log detected but test passed
- FeedbackScreen modified but not covered by UI/E2E test
- Subscription feature enabled for vetclinic-blue, but not tested

---

> QA Engineer Agent • Policy: failOnRegression=true • Manual override not permitted

🔐 Escalation Behavior (based on policy)¶

Policy Flag	Result
`allowManualOverride: false`	Block CI, halt release, route to HumanOps
`allowManualOverride: true`	Route to QA Manager or Studio for confirmation
`requireHumanApprovalOnEditionDrift: true`	Force manual review for edition-specific issues

📤 Notification Artifacts¶

manual-review-needed.md
PR comment or Studio alert
Event: QAReviewEscalationTriggered
Links to qa-summary.json, regression-matrix.json, relevant test logs or trace logs

📎 HumanOps & QA Manager Actions¶

Action	Method
✅ Approve override	Submit `override-approval.yaml` in PR or Studio
❌ Reject build	Comment or tag `build:blocked`
📝 Annotate issue	Add to `studio.qa.annotations.json` or test backlogs

🧠 Agent Behavior After Escalation¶

Marks build as requires-review
Flags unapproved build in CI/CD system
Waits for response from HumanOps or timeout-based fallback (if configured)

🛑 Studio Display (Escalation Mode)¶

🟡 Yellow "Requires Manual QA Review" banner
🔎 Viewable list of all escalation reasons
📝 Input field for QA manager annotations
🚦 Buttons: Approve, Block, Re-run Tests

✅ Summary¶

The QA Engineer Agent:

🧑‍💻 Detects when automation is insufficient
🛑 Blocks or warns on critical gaps
📄 Emits clear, traceable escalation artifacts
👥 Invokes structured human review with Studio + PR integration

This supports quality-first autonomy with safety rails — aligning AI-based validation with human-approved governance in the ConnectSoft pipeline.

🤝 Collaboration Interfaces¶

This section outlines how the QA Engineer Agent integrates and collaborates with other agents across the ConnectSoft AI Software Factory to:

Validate test results and execution
Evaluate quality in tandem with runtime behavior
Route defects, gaps, or instability to the proper collaborators
Inform Studio dashboards and the CI/CD ecosystem

🧩 Core Collaboration Map¶

Collaborating Agent	Collaboration Type	Description
🧪 Test Automation Engineer Agent	Test Executor	Runs tests and emits structured results consumed by QA
🤖 Test Generator Agent	Test Creator	Builds BDD, E2E, and unit test cases QA uses to validate coverage
🧬 Bug Investigator Agent	Post-Failure Analyzer	Receives flagged regressions or unstable failures from QA
📊 Observability Agent	Runtime Signal Provider	Sends crash logs, unhandled exceptions, untested spans
🌪 Resiliency & Chaos Engineer Agent	Fault Validator	Sends chaos test results and failure impact levels
🧱 Code Reviewer Agent	Change Delta Provider	Annotates changed code regions QA verifies for drift coverage
🎭 Edition Coordinator Agent	QA Scope Provider	Defines edition-specific routes and features to validate
📦 CI/CD Agent	Gatekeeper	Reads QA verdicts to block/allow builds and promote to release
👤 HumanOps Agent	Manual Escalation Handler	Receives manual-review flags from QA for human triage
🖥 Studio Dashboard Agent	Visual Reporter	Renders QA coverage, score, and regression metrics for stakeholders

🔄 Collaboration Workflow (Simplified)¶

sequenceDiagram
    participant Gen as Test Generator
    participant Auto as Test Automation Agent
    participant Obs as Observability Agent
    participant QA as QA Engineer Agent
    participant Bug as Bug Investigator Agent
    participant Studio as Studio Dashboard Agent
    participant CI as CI/CD Agent

    Gen->>Auto: Generated tests
    Auto->>QA: test-results.json
    Obs->>QA: trace-logs.json
    QA->>Bug: regressions, flakiness
    QA->>Studio: qa-summary.json, regression-matrix
    QA->>CI: pass/fail + confidence score

Hold "Alt" / "Option" to enable pan & zoom

📤 Outputs Shared With Collaborators¶

Output File	Consumed By	Purpose
`qa-summary.json`	CI/CD Agent, Studio	Build verdict, pass/fail/score
`regression-matrix.json`	Bug Investigator Agent	Identify regressions or test flakiness
`test-gap-report.yaml`	Test Generator Agent	Suggest additional test creation
`manual-review-needed.md`	HumanOps Agent	Guide manual QA decisions
`studio.qa.status.json`	Studio Agent	Visual dashboard + CI indicators
`qa-overview.md`	Developer PR summary	Quick QA health check

🧠 Input Artifacts Received From Agents¶

Agent	Artifact
Test Generator	`test-plan.yaml`, `screen-test-map.json`
Test Automation	`test-results.json`, `test-timing.json`
Observability	`trace-logs.json`, `unhandled-exceptions.json`
Chaos Agent	`chaos-impact-report.json`
Bug Investigator	`known-regressions.yaml`, `flaky-tests-index.yaml`
Edition Coordinator	`edition-config.yaml`, `edition-policy-overrides.yaml`

🔁 Cross-Agent Event Hooks¶

Event	Target Agent
`RegressionDetected`	Bug Investigator
`TestGapIdentified`	Test Generator
`QAVerdictPublished`	CI/CD Agent, Studio
`ManualReviewRequired`	HumanOps Agent

✅ Summary¶

The QA Engineer Agent:

🤝 Orchestrates collaboration with execution, analysis, and governance agents
📤 Shares artifacts that influence regression analysis, test planning, and CI decisions
🧠 Consumes structured input from test runners, trace collectors, edition planners
📈 Enables Studio dashboards and policy-driven quality gates

This makes the QA Engineer Agent the hub of quality enforcement and intelligence in the AI-driven delivery lifecycle.

📈 Observability-Driven QA¶

In This section, we define how the QA Engineer Agent leverages observability signals (telemetry, logs, spans, and runtime errors) to:

Identify gaps in test coverage
Detect issues not caught by test assertions
Strengthen QA verdicts using production-like behavior validation

This approach ensures quality validation is not test-only, but also behavior-aware.

🔍 Observability Signals Used¶

Signal	Source	Used For
OpenTelemetry Spans	Observability Agent	Detect coverage gaps (e.g. screens used in prod but never tested)
Unhandled Exceptions	Crash reporting/logs	Flag runtime crashes not triggered by tests
API Failure Logs	4xx/5xx traces	Highlight untested or unstable backend behavior
Screen Transition Logs	Frontend span traces	Identify untraced screen flows
Latency/Load Trends	Performance Agent or Observability Agent	Catch instability from slow or unresponsive flows

📘 Sample: `trace-logs.json`¶

{
  "unhandledErrors": [
    {
      "screen": "FeedbackScreen",
      "error": "NullReferenceException",
      "traceId": "span-ff1234",
      "userImpact": "high"
    }
  ],
  "untestedSpans": [
    "BookingSuccessScreen",
    "SubscriptionCheckout"
  ],
  "apiFailRates": {
    "/login": 0.01,
    "/submit-feedback": 0.23
  }
}

→ QA Agent uses this to reduce confidence score, and emits suggestions to Test Generator Agent.

🧠 Observability-Supported QA Enhancements¶

Use Case	QA Agent Behavior
Screen shows crash in span but test suite passes	Emit warning: “Test missing crash case for FeedbackScreen”
API has 20% failure rate in logs but marked “passed”	Reduce confidence score and suggest retry
Spans indicate routing to screen never tested	Add to `test-gap-report.yaml`
Chaos/latency-induced error seen in trace	Emit `ManualReviewRequired` if above threshold

🧩 Observability Hooks per Test Type¶

Test Type	Augmented By Observability?	Action
E2E	✅ Yes	Trace screen navigation, crashes, hangs
Integration	✅ Yes	Compare span vs coverage for API endpoints
UI	⚠️ Partial	Check for unobserved transitions (e.g. missing testId)
Unit	❌ No	Not traceable at runtime level

📊 QA Report Adjustments¶

Field	Example
`observabilityWarnings`	`true`
`missingRuntimeSpans`	`["SubscriptionCheckout"]`
`crashInUntestedScreen`	`FeedbackScreen`
`adjustedConfidenceScore`	`-0.05` from observability drift

📄 QA Report Output Snippet¶

{
  "confidenceScore": 0.86,
  "observabilityDrift": true,
  "untestedRuntimeScreens": ["SubscriptionCheckout"],
  "crashDetectedNotCoveredByTest": "FeedbackScreen"
}

📎 Studio QA Tile Effects¶

🔥 Crash or trace errors raise visibility in dashboard
🛠️ Missing trace coverage marks screen as “test recommended”
📉 Observability-induced confidence drop is tagged and explained

✅ Summary¶

The QA Engineer Agent:

📈 Ingests runtime telemetry as a QA signal
🧠 Detects hidden issues not visible to tests
🔎 Identifies runtime flows or APIs never tested
📉 Adjusts scoring and QA decisions based on behavior data

This enables observability-enhanced quality validation, delivering higher confidence in releases — even in complex, multi-agent mobile or API systems.

🧾 Tenant/Edition QA Strategy¶

This section defines how the QA Engineer Agent validates tenant-specific and edition-specific functionality — ensuring that white-labeled apps, regional variants, or multi-tenant SaaS features are explicitly test-covered and safe for release.

🎭 Why Tenant/Edition QA Matters¶

In ConnectSoft’s platform:

Different editions (e.g., vetclinic-premium, wellness-lite) may enable or disable features, screens, branding, or flows
Different tenants may have legal, regulatory, or product-based differences
QA must verify that each edition’s declared functionality is appropriately tested and stable

📘 Sample: `edition-config.yaml`¶

editionId: vetclinic-blue
tenantId: vetclinic-premium
features:
  enableChat: false
  enableAppointments: true
screens:
  include: [LoginScreen, Appointments, ProfileScreen]
  exclude: [MarketingConsentScreen]

→ QA Agent validates that Appointments is covered, and MarketingConsentScreen is ignored.

✅ QA Scope Enforcement¶

Dimension	QA Responsibility
Enabled Feature Testing	Ensure enabled features/screens are tested
Disabled Feature Skipping	Ensure tests do not assert screens not visible in this edition
Tenant Branding Tests	Confirm UI screens render with correct theme, font, logo
Legal Requirements by Region	Validate presence of policy/consent screens, GDPR, etc.
Split Routes by Edition	Confirm navigation differences per edition are tested

🧩 Artifacts for Edition QA¶

File	Used For
`edition-policy-overrides.yaml`	Defines QA constraints per edition
`edition-test-map.json`	Maps edition → required screens and flows
`test-results.json`	Must include edition-contextual test run metadata
`qa-summary.json`	Includes `editionCoverageScore`, `editionViolations[]`

📘 Sample: `edition-policy-overrides.yaml`¶

edition: vetclinic-blue
requiredScreens:
  - Appointments
  - LoginScreen
excludedScreens:
  - ChatSupport
requiredE2EFlows:
  - AppointmentBooking
  - LoginWithEmail
branding:
  theme: vetclinic-dark

📊 Edition Coverage Scoring¶

{
  "editionId": "vetclinic-blue",
  "requiredScreens": 4,
  "testedScreens": 3,
  "coverage": 75,
  "violations": ["ChatScreen test present but excluded", "GDPRConsentFlow missing"]
}

→ Low score leads to status: requires-review.

📘 Output Snippet from `qa-summary.json`¶

{
  "editionCoverageScore": 0.75,
  "editionComplianceStatus": "violated",
  "violations": [
    "MarketingConsentScreen was tested but excluded in edition config",
    "LoginWithEmail flow failed on B2C-only edition"
  ]
}

🧠 Edition-Aware Scenarios Checked¶

QA Area	Check
Screens	Present, excluded, tested as intended
Feature flags	Respected in flow tests
Theming	Visual branding assertions passed
Legal content	Present or exempted
API features	Edition-bound APIs are tested or skipped properly

✅ Summary¶

The QA Engineer Agent:

📋 Enforces per-edition QA scope
🧪 Validates branding + feature coverage
❌ Flags cross-edition test violations
📈 Scores edition QA coverage and compliance
📄 Emits edition-specific QA metadata for dashboards and releases

This supports safe, compliant multi-tenant SaaS delivery at scale — with traceable, test-verified edition overlays.

📱💻🔌 Mobile/Web/API QA Flows¶

In This section, we define how the QA Engineer Agent validates software quality across multiple delivery surfaces — including mobile apps, web frontends, and backend APIs — ensuring functional consistency and completeness across channels.

🧩 Surfaces Covered¶

Surface	Channels
Mobile	.NET MAUI, Flutter, React Native
Web	Angular, Blazor, React
API	REST (OpenAPI), GraphQL, gRPC
Backend Flows	Async pipelines, event handlers, message contracts
Edge	Auth flows, identity delegation, tenant switching

🎯 QA Responsibilities per Surface¶

Surface	QA Expectations
Mobile	Screen-level E2E, platform-specific routing, edition overlays, telemetry
Web	Route-based flow validation, UI component testing, localization checks
API	Endpoint coverage, error contract validation, untested 4xx/5xx paths
Backend Flows	Retry/failure coverage, event-driven testing, saga orchestration paths
Cross-surface	Shared screen state, session flows, auth transitions between mobile/web/API

📘 Multi-Surface Test Example (Appointment Flow)¶

Step	Surface	Test
Start app → login → dashboard	Mobile	E2E (detox / UI test)
Book appointment via API	API	Contract test + response validation
Verify UI shows success	Web (if multi-surface)	Component snapshot + state assertion
Check appointment in backend queue	Backend	Integration + event trace
Confirm analytics event emitted	Observability	Telemetry span check

📊 Surface Coverage Analysis¶

The QA Engineer Agent tracks coverage per surface:

{
  "mobileCoverage": 92.3,
  "webCoverage": 81.5,
  "apiCoverage": 95.4,
  "backendFlowCoverage": 78.2,
  "crossSurfaceGaps": ["LogoutSessionInvalidation", "ProfileSync"]
}

→ These scores are used in confidenceScore and Studio dashboard analytics.

📎 Surface-Aware Report Fields (`qa-summary.json`)¶

{
  "surfaceCoverage": {
    "mobile": 0.92,
    "web": 0.82,
    "api": 0.96
  },
  "crossSurfaceViolations": ["SessionDrift", "MissingTenantSwitchTest"]
}

🛠️ Special QA Actions for APIs¶

Verify 2xx, 4xx, and 5xx flows are tested
Confirm auth headers and multitenancy logic are validated
Assert contract response matches OpenAPI or schema snapshot
Detect versioned API endpoints missing tests (e.g., v2/appointments)

🧠 API Drift Detection¶

The QA Engineer Agent compares:

openapi-v1.yaml vs openapi-v2.yaml
Contract test coverage across changed paths
Flags any untested newly added endpoints or updated response schemas

📄 QA Output: API Validation Snippet¶

{
  "apiCoverage": 95.4,
  "untestedEndpoints": ["/cancel-appointment", "/reset-password"],
  "contractMismatchDetected": true,
  "multiVersionCoverage": {
    "v1": 100,
    "v2": 86.7
  }
}

✅ Summary¶

The QA Engineer Agent:

🧪 Verifies tests span across mobile, web, and backend APIs
📈 Scores each surface independently + composite confidence
🔎 Detects drift or gaps across shared flows
📄 Emits detailed QA artifacts across multiple delivery channels

This ensures end-to-end user and system flows are verifiably covered — regardless of delivery surface or interface.

🔄 Build QA Status Lifecycle¶

This section defines how the QA Engineer Agent manages the lifecycle of QA status per build, from initialization to final verdict. It enables automated quality tracking and decision-making across CI/CD, Studio, and multi-agent pipelines.

🧭 Build QA Status States¶

Status	Meaning
`pending`	QA analysis has not yet been completed
`in-progress`	Agent is validating results, coverage, regressions
`pass`	QA conditions are satisfied, build is quality-approved
`fail`	QA conditions failed (low score, regressions, coverage)
`requires-review`	Borderline score or test gap requires human approval
`skipped`	QA bypassed due to config override or known exception

🔁 State Transition Flow¶

stateDiagram-v2
    [*] --> pending
    pending --> in-progress: QA started
    in-progress --> pass: All validations succeed
    in-progress --> fail: Regressions or insufficient coverage
    in-progress --> requires-review: Score borderline or manual review triggered
    requires-review --> pass: Human override accepted
    requires-review --> fail: Human rejected or timeout
    pass --> [*]
    fail --> [*]

Hold "Alt" / "Option" to enable pan & zoom

📂 Artifacts Created Per Stage¶

Stage	Artifact
`pending`	`build-qa-init.json`
`in-progress`	`qa-processing.log`, live `confidenceScore` updates
`pass`	`qa-summary.json`, `qa-overview.md` with ✅
`fail`	`qa-summary.json`, `manual-review-needed.md`, regression matrix
`requires-review`	`manual-review-needed.md`, `studio.qa.review.flags.json`

📘 Example: Build Status Block¶

{
  "buildId": "connectsoft-mob-v5.3.0",
  "traceId": "proj-812-v1",
  "status": "requires-review",
  "confidenceScore": 0.82,
  "regressions": ["LoginWithInvalidEmail"],
  "untestedChanges": ["FeedbackScreen"],
  "lastUpdated": "2025-05-15T22:20:00Z"
}

🛡️ QA Gate Enforcement Rules¶

Trigger	Action
`status == fail`	Block CI/CD, alert orchestrator
`status == requires-review`	Pause release, notify QA Manager/HumanOps
`status == pass`	Mark build green in Studio and pipelines
`timeout on review > 24h`	Escalate or auto-reject depending on policy

📦 Integration with CI/CD and Studio¶

CI/CD Agents poll qa-summary.json and build-qa-status.json before release
Studio Dashboards use studio.qa.status.json to color-code builds and show QA metadata
HumanOps Agent watches for escalation or override triggers via qa-review-needed.md

🔁 Multiple QA Checks Per Build¶

For multi-platform or multi-edition builds, each may have its own QA status:

{
  "bookingapp-v5.3.0": {
    "flutter": { "status": "pass", "score": 0.91 },
    "react-native": { "status": "requires-review", "score": 0.82 },
    "maui": { "status": "fail", "score": 0.76 }
  }
}

→ Aggregated for orchestration; partitioned by platform in QA summary.

✅ Summary¶

The QA Engineer Agent manages the complete QA state machine for each build:

Tracks status per platform, tenant, edition
Transitions based on validation, policy, and escalation
Integrates with Studio dashboards and CI/CD agents
Ensures traceability, automation, and optional human override

This enables continuous, reliable QA enforcement at scale — with clear, observable lifecycle transitions.

🚀 CI/CD QA Hooks¶

This section defines how the QA Engineer Agent integrates with CI/CD pipelines, enforcing release safety by injecting quality checks, emitting pass/fail verdicts, and communicating with pipeline orchestrators, PR validation tools, and Studio.

🎯 Goals of QA Hooks in CI/CD¶

Block or allow release based on QA status
Expose coverage, score, and regression metadata in PRs
Route failed or risky builds for human approval
Integrate seamlessly with GitHub Actions, Azure Pipelines, Bitrise, Codemagic, and custom runners

🧩 Integration Points¶

Integration Layer	Hook Type	Behavior
🛠️ Build Stage	`qa-summary.json` check	Fail job if `status: fail` or score too low
🔁 PR Validation	Markdown summary comment	Posts `qa-overview.md` with coverage, regressions, warnings
🧠 Manual Review	PR comment or Studio signal	Waits for override/approval via `override-approval.yaml` or UI
🧾 Release Workflow	Artifact check	Publishes only if `status: pass` or override accepted
📊 Dashboard Stage	QA status tile update	Pushes QA report to Studio via `studio.qa.status.json`

📘 GitHub Actions Example (QA Check)¶

- name: Load QA verdict
  run: |
    score=$(jq .confidenceScore qa-summary.json)
    status=$(jq -r .status qa-summary.json)
    if [ "$status" = "fail" ]; then
      echo "❌ QA failed: Score = $score"
      exit 1
    fi

📘 QA Status Badge in PR (Markdown)¶

### 🧪 QA Summary
- **Status**: ❗ Requires Review  
- **Confidence Score**: 0.82  
- **Regressions**: 2  
- **Untested Modules**: FeedbackScreen, CancelFlow  
- [Full QA Report →](link-to-artifact)

> Triggered by QA Engineer Agent • Trace: proj-811-v2 • Edition: vetclinic-premium

📦 Artifacts Used in Pipelines¶

File	Purpose
`qa-summary.json`	Machine-readable verdict
`qa-overview.md`	PR comment or Studio upload
`regression-matrix.json`	Shown in Studio and build dashboard
`test-gap-report.yaml`	Forwarded to Test Generator Agent
`manual-review-needed.md`	Causes CI pause or notification

🧠 Exit Codes & Status Propagation¶

Status	CI Action
`pass`	Continue pipeline
`fail`	Exit with non-zero; block release
`requires-review`	Pause and await override (Studio/PR)
`skipped`	Skip validation (allowed only in exception mode)

📎 QA Flags for CI Environments¶

Flag	Purpose
`qa.enabled=true`	Ensures QA agent is invoked in pipeline
`qa.strict=true`	Prevent override unless explicitly configured
`qa.edition=vetclinic-blue`	Scope QA to a specific edition in multitenant pipelines
`qa.allowRetry=true`	Allows retry-on-failure for transient issues (e.g., flaky tests)

✅ Summary¶

The QA Engineer Agent includes:

🚦 Pass/fail hooks for CI pipelines
📋 Markdown-based PR QA summaries
📊 Dashboard status propagation via Studio
⏸️ Human review integration for overrides
🔐 Secure, policy-enforced release gating

This guarantees automated QA governance inside ConnectSoft’s CI/CD flow — with clear, explainable outcomes at every stage.

🐞 Bug Feedback Loop¶

In This section, we define how the QA Engineer Agent collaborates with the Bug Investigator Agent and other feedback channels to manage:

Regressions
Flaky or inconsistent test results
Coverage-related bugs
Reopened or reoccurring issues

The goal is to maintain high signal fidelity in QA verdicts while enabling autonomous debugging workflows.

🔁 Feedback Loop Trigger Conditions¶

Trigger	Result
✅ Regression detected	QA Agent notifies Bug Investigator Agent
🔄 Flaky test identified	QA marks test as unstable, sends it for triage
🧪 Missing coverage on failing feature	QA emits `test-gap-report.yaml` + `regression-matrix.json`
🧠 Crash in runtime logs (not covered by test)	QA flags and opens investigation
❌ Reopened bug previously marked fixed	QA score penalized and bug trace tagged

🧩 Key Collaborator: Bug Investigator Agent¶

The Bug Investigator Agent:

Analyzes regressions sent by QA Agent
Confirms flakiness, crash root cause, or false positive
Updates regression index
Suggests test stabilization or code rollback

📘 Example: QA → Bug Investigator Handoff¶

{
  "trigger": "RegressionDetected",
  "testCaseId": "LoginWithWrongPassword",
  "buildId": "bookingapp-v5.3.1",
  "regressedModule": "authService",
  "flakyHistory": 2/5 recent runs,
  "confidenceImpact": -0.05,
  "traceId": "proj-814-v1"
}

📄 Output from QA for Bugs¶

File	Purpose
`regression-matrix.json`	Lists repeated and new regressions
`flaky-tests-index.yaml`	Flags test cases with instability
`test-gap-report.yaml`	Suggests where test creation is needed
`manual-review-needed.md`	Summarizes bugs requiring human attention

🧠 Memory Updates¶

The QA Engineer Agent updates:

Known regressions memory (for scoring)
Ignored flakiness list (if approved)
Test impact map (to prioritize generation or automation)

🎯 Studio & CI Feedback Integration¶

QA Finding	Outcome
Regression marked flaky by Bug Investigator	Build allowed but noted as unstable
Regression confirmed real	QA verdict remains fail or review
Regression tagged as false positive	Confidence score restored
Bug marked “needs test”	Test Generator Agent is triggered
Bug resolution verified	Regression is removed from memory

📘 Flaky Test Tracking Example¶

flakyTests:
  - testId: DeleteAccountFlow
    failureRate: 30%
    lastFail: bookingapp-v5.2.9
    suggestedFix: Increase delay before final step

✅ Summary¶

The QA Engineer Agent supports a full bug investigation feedback loop:

🐞 Forwards regressions, crashes, and flaky tests
🤝 Collaborates with Bug Investigator Agent for root cause
📉 Adjusts scoring and verdicts dynamically
📦 Enables a self-healing, evidence-based QA ecosystem

This ensures resilient QA logic, smarter test prioritization, and AI-driven triage in ConnectSoft pipelines.

📚 Test Artifact Curation¶

This section defines how the QA Engineer Agent manages and curates test execution artifacts, including:

QA-approved test results
Known stable/unstable tests
Annotated gaps
Regression memory
Edition-aware test data

These artifacts serve as a living QA knowledge base, enabling reproducibility, auditability, and continuous improvement of the test suite.

🗂️ Artifact Types Maintained¶

Artifact	Description
`qa-summary.json`	Final QA decision per build (pass/fail/review)
`test-results.json`	Full test execution report, categorized
`coverage-summary.json`	Type- and module-specific coverage breakdown
`regression-matrix.json`	Known regressions, fixed-but-unverified tests
`flaky-tests-index.yaml`	Catalog of known unstable or inconsistent tests
`test-gap-report.yaml`	Areas of missing test coverage
`studio.qa.status.json`	Output for dashboards, metadata trace tagging
`edition-test-map.json`	Screens, routes, features tested per edition
`manual-review-needed.md`	Markdown summary of flagged areas needing review

🧠 Curation Behaviors¶

Behavior	Outcome
Hash test outputs	Detect duplicate/unchanged results between runs
Merge with regression memory	Track trends across builds
Retain known flaky metadata	Prevent false blocks from intermittent failures
Annotate test gaps with suggestions	Direct inputs to Test Generator Agent
Store per-edition coverage	Ensure tenant-specific QA safety nets are tracked separately

📘 Example: `flaky-tests-index.yaml`¶

flakyTests:
  - testId: FeedbackFormEmptySubmit
    failRate: 40%
    resolution: retry suggested
  - testId: PaymentTimeout
    failRate: 30%
    allowedOverride: true
    manualConfirmationLastRun: booking-v5.2.3

📘 Example: `edition-test-map.json`¶

{
  "vetclinic-blue": {
    "screensTested": ["LoginScreen", "Appointments", "ProfileScreen"],
    "excludedScreens": ["MarketingLanding", "ChatSupport"],
    "coverageScore": 88.3
  }
}

🔁 Versioned Test Memory¶

Artifacts are stored:

Per build (buildId, traceId)
Per platform (flutter, maui, react-native)
Per edition and tenant
With confidence metadata and coverage metrics

🔒 Compliance & Traceability¶

Test artifacts are:

✅ Immutable per release
📁 Stored for audit and rollback
🧾 Exportable to Studio or external systems for governance

📦 Storage Integration Options¶

Location	Used For
`qa-artifacts/{buildId}/`	Full build test trace
`qa-memory/known-flaky.yaml`	Shared across builds
`studio.qa.status.json`	Consumed by Studio dashboards
`test-gaps/pending.yaml`	Consumed by Test Generator Agent

✅ Summary¶

The QA Engineer Agent:

📁 Curates structured test artifacts across modules and editions
🧠 Maintains memory of known regressions, flakiness, gaps
📤 Shares artifacts with Test Generator, Bug Investigator, Studio
🧾 Provides a reproducible QA state per build

This enables traceable, memory-enriched QA validation, enhancing the effectiveness of every future QA cycle and agent collaboration.

🖥️ Studio Dashboard Outputs¶

This section explains how the QA Engineer Agent exports QA results to Studio dashboards, enabling developers, QA leads, and product owners to visualize:

Build quality and confidence scores
Test coverage by screen/module/edition
Regressions and unstable flows
Status of QA reviews and manual escalations

🎯 Studio Dashboard Goals¶

Visualize pass/fail status across editions, platforms, and features
Trace quality over time and across builds
Highlight regressions, test gaps, and unstable tests
Surface edition-specific QA violations
Provide human-readable summaries for decision-making

📦 Dashboard Input Artifacts¶

File	Purpose
`studio.qa.status.json`	QA status tile data (build, score, status)
`qa-summary.json`	Raw verdict, test count, confidence score
`qa-overview.md`	Readable Markdown summary (shown on hover or click)
`test-gap-report.yaml`	Highlight missing coverage in Studio test matrix
`regression-matrix.json`	Visualize regressions and trend lines
`flaky-tests-index.yaml`	Flag test cases as unstable in test explorer
`edition-test-map.json`	Coverage heatmap per edition/tenant
`manual-review-needed.md`	Studio review banner and action panel trigger

🖥️ Dashboard Tiles and Widgets¶

Tile	Description
🟢 QA Status	Pass / Fail / Requires Review — per build or platform
📈 Confidence Score	% with trend line and history view
🔍 Test Coverage	Unit, integration, E2E, UI breakdown
🧱 Screen Heatmap	Screens/modules with coverage or gaps
🔁 Regression Tracker	Shows repeated failures and new issues
🔄 Edition Compliance	QA coverage of edition-bound screens/features
🧪 Flaky Test Radar	Alerts for instability or frequent failure cases
👤 Manual Review Panel	Displays flagged builds requiring override or feedback

📘 Sample: `studio.qa.status.json`¶

{
  "buildId": "bookingapp-v5.3.0",
  "traceId": "proj-814-v2",
  "platform": "flutter",
  "status": "pass",
  "confidenceScore": 0.91,
  "regressions": 0,
  "coverage": {
    "unit": 83.1,
    "integration": 75.0,
    "e2e": 66.2
  },
  "editionCompliance": {
    "status": "ok",
    "score": 89.7
  }
}

📋 Studio UI Interactions Supported¶

Action	Result
🔍 Click build QA tile	Opens QA summary + test report
📝 Hover confidence score	Shows detailed score breakdown
⚠️ See regression icon	Opens regression matrix and links to Bug Investigator
🔓 Override button (if enabled)	Sends signal to CI/CD + HumanOps Agent
🧪 Test Gaps tab	Filters screens/modules with low or no coverage

🔄 Live Updates & Trends¶

QA Agent pushes updated scores during in-progress phase
Dashboard shows real-time changes in verdict, status, and regressions
Trend lines across builds help QA leads spot drift or stability issues

🧠 Insight Generation (Future)¶

Planned future metrics:

Risk-weighted score by surface (e.g., login, onboarding)
Per-feature quality score (Bookings, Payments, Chat)
Edition differential QA (highlight what’s covered in one edition but not another)

✅ Summary¶

The QA Engineer Agent:

📊 Publishes rich QA metadata to Studio
🧱 Powers tiles, trends, and test explorer UIs
📤 Exposes regressions, test gaps, and edition QA issues visually
🧑‍💻 Enables QA teams and HumanOps to take guided actions

Studio dashboards become the source of truth for QA confidence, quality drift, and readiness decisions.

🧭 Final Blueprint & Future Direction¶

This final section consolidates the architecture, responsibilities, and strategic trajectory of the QA Engineer Agent within the ConnectSoft AI Software Factory. It also outlines future enhancements to make the QA pipeline more intelligent, autonomous, and scalable across thousands of SaaS features and multi-tenant editions.

🧱 QA Engineer Agent Blueprint¶

flowchart TB
  subgraph Inputs
    TGA[Test Generator Agent]
    TAA[Test Automation Agent]
    OBS[Observability Agent]
    CHAOS[Chaos Engineer Agent]
    BUG[Bug Investigator Agent]
    EDITION[Edition Coordinator Agent]
  end

  subgraph QA[[QA Engineer Agent]]
    direction TB
    Skills[
      ValidateBuildQualitySkill
      ComputeConfidenceScoreSkill
      AnalyzeCoverageSkill
      DetectRegressionSkill
      GenerateQAReportsSkill
    ]
  end

  Inputs --> QA
  QA --> STUDIO[Studio Dashboard Agent]
  QA --> CI[CI/CD Agent]
  QA --> HUMAN[HumanOps Agent]
  QA --> BUG
  QA --> TGA

Hold "Alt" / "Option" to enable pan & zoom

🧠 Summary of Capabilities¶

Area	Description
Test Result Analysis	Aggregates from multiple agents and runners
Regression & Flakiness Detection	Identifies recurring or unstable issues
Confidence Scoring	Combines test pass %, coverage, regressions, and observability
Edition-Specific QA Enforcement	Ensures per-edition functionality is correctly tested
Studio + CI/CD Integration	Blocks, escalates, or approves builds
Manual Review Flow	Escalation mechanism with structured inputs
Artifact Curation	Structured storage of QA knowledge over time

📂 QA Artifact System¶

Artifact	Purpose
`qa-summary.json`	Verdict: pass/fail/score
`test-gap-report.yaml`	Coverage holes
`regression-matrix.json`	Regressions & drift
`flaky-tests-index.yaml`	Unstable test catalog
`edition-test-map.json`	Per-edition validation tracking
`studio.qa.status.json`	Studio dashboard export

🔮 Future Directions¶

✅ Short-Term Enhancements¶

Idea	Benefit
Risk-weighted scoring	Prioritize test coverage on critical flows
Flaky test auto-isolation	Improve stability of CI pipelines
Studio QA insights API	Programmatic access to QA health per build
Automated recovery triggers	Suggest test regen or retries when test failure reason is known

🌐 Mid-Term Strategic Expansion¶

Direction	Details
Visual QA Validator Agent	Adds image-based visual diffs + perceptual regressions
Synthetic QA Planning Agent	Simulates missing test logic based on observability traces
Zero-touch rollback integration	Revert builds if QA + post-release tracing detects a regression
Proactive Drift Reporter	Alerts module owners about under-tested or unstable areas based on trend analysis

🚀 Long-Term Vision¶

Autonomous QA-as-a-Service embedded into every ConnectSoft project, with per-feature scoring, edition-aware validation, and test lifecycle traceability — all managed and evolved by AI agents.

✅ Final Summary¶

The QA Engineer Agent is:

🧪 The central validator of quality across all delivery channels
🤖 Integrated into CI/CD, Studio, and agent orchestration
📈 Driven by test evidence, observability, and policies
🧠 Memory-enhanced and drift-aware
🧾 Structured and traceable for every tenant, edition, and build

It provides autonomous QA oversight at scale, making ConnectSoft releases quality-verified, test-tracked, and continuously improving.

🧪 QA Engineer Agent Specification¶

🎯 Purpose¶

🧭 Strategic Role in ConnectSoft AI Software Factory¶

🔁 Agent Placement in QA Flow¶

🎯 What the QA Engineer Agent Guarantees¶

🧱 Quality Philosophy¶

🔐 Compliance & Non-Functional Scope¶

🧑‍💻 HumanOps Role¶

✅ Summary¶

📋 Core Responsibilities¶

🧭 Primary Responsibilities¶

🗂️ Reported Outputs (Preview)¶

📘 Sample QA Output Snippet¶

🚦 Validation Scope Types¶

🧑‍💻 Developer-Centric Interactions¶

✅ Summary¶

📥 Inputs Consumed¶

🧩 Structured Inputs by Source¶

📘 Sample: coverage-summary.json¶

🧠 Semantic Inputs (via SK Prompt / Memory)¶

🧪 Test Type Classification¶

🔎 QA Policy Input (qa-policy.yaml)¶

🌍 Edition-Specific Overrides¶

✅ Summary¶

📤 Outputs Produced¶

📁 Primary Output Artifacts¶

📘 Sample: qa-summary.json¶

📘 Sample: qa-overview.md¶

📘 Sample: test-gap-report.yaml¶

📊 Output Tags and Traceability¶

🚦 CI/CD Output Behavior¶

📤 Studio/Orchestrator Integration¶

✅ Summary¶

🔄 Execution Flow¶

🔁 High-Level Execution Pipeline¶

🪜 Execution Phase Breakdown¶

📘 Example: Build Confidence Calculation¶

🧑‍💻 Escalation Flow (if needed)¶

📋 Execution Metadata Output¶

🧠 Determinism & Repeatability¶

✅ Summary¶

🧩 Skills and Semantic Kernel Functions¶

🧠 Core Semantic Kernel Skills¶

📘 Sample Skill Call – ComputeConfidenceScoreSkill¶

📎 Trace Metadata Injected by Skills¶

🔁 Skill Reuse Across Agents¶

🛠️ Skill Customization Based on Policy¶

✅ Summary¶

📈 Test Coverage Management¶

🎯 Types of Coverage Tracked¶

📘 Sample Input: coverage-summary.json¶

🧪 Coverage Threshold Rules¶

📄 Example QA Policy Fragment¶

📍 Coverage by Entity¶

🔎 Test Coverage Gaps → test-gap-report.yaml¶

📊 Heatmap Metadata for Studio¶

✅ Summary¶

📋 Validation Policies & Checklists¶

📄 QA Policy Source¶

✅ Default QA Policy Rules¶

📘 Sample: qa-policy.yaml¶

📋 Edition-Specific Checklist (from edition-policy-overrides.yaml)¶

🧪 Additional Checklists Validated¶

📄 QA Gate Decision Heuristics¶

📎 Visual Display in Studio¶

✅ Summary¶

🔁 Regression and Drift Detection¶

🔍 Types of Regressions Detected¶

🧠 Regression Memory¶

📘 Sample: regression-index.yaml¶

🔁 Detection Algorithm¶

📘 Sample Drift Report (regression-matrix.json)¶

🎯 Outputs Affected¶

📘 qa-summary.json with Regression Flag¶

🛑 Studio Display¶

✅ Summary¶

🧑‍💻 Human-Aware Escalation Points¶

🎯 Escalation Triggers¶

📘 Escalation Output: manual-review-needed.md¶

🔐 Escalation Behavior (based on policy)¶

📘 Sample: `coverage-summary.json`¶

🔎 QA Policy Input (`qa-policy.yaml`)¶

📘 Sample: `qa-summary.json`¶

📘 Sample: `qa-overview.md`¶

📘 Sample: `test-gap-report.yaml`¶

📘 Sample Skill Call – `ComputeConfidenceScoreSkill`¶

📘 Sample Input: `coverage-summary.json`¶

🔎 Test Coverage Gaps → `test-gap-report.yaml`¶

📘 Sample: `qa-policy.yaml`¶

📋 Edition-Specific Checklist (from `edition-policy-overrides.yaml`)¶

📘 Sample: `regression-index.yaml`¶

📘 Sample Drift Report (`regression-matrix.json`)¶

📘 `qa-summary.json` with Regression Flag¶

📘 Escalation Output: `manual-review-needed.md`¶

📘 Sample: `trace-logs.json`¶

📘 Sample: `edition-config.yaml`¶

📘 Sample: `edition-policy-overrides.yaml`¶

📘 Output Snippet from `qa-summary.json`¶

📎 Surface-Aware Report Fields (`qa-summary.json`)¶