---

name: quality-regression-testing description: > Use when the user asks about regression testing, test selection, regression suite design, automation maintenance, flaky tests, or regression metrics. Do NOT use for: smoke/BVT testing (quality-smoke-testing), acceptance testing (quality-acceptance-testing), or exploratory testing (quality-exploratory-testing). version: "2.0.0" author: "j4flmao" license: "MIT" compatibility: claude-code: true cursor: true codex: true windsurf: true tags: [quality, regression-testing, phase-6]

Regression Testing

Purpose

Ensure that new code changes do not break existing functionality through effective test selection, suite design, automation maintenance, and metric-driven optimization. This skill covers impact analysis, risk-based selection, prioritization, flaky test management, and CI integration.

Agent Protocol

Trigger

User mentions regression testing, test selection, regression suite, flaky tests, automation maintenance, or asks what to re-test after a change.

Input Context

• Code changes (commits, PRs, commits since last regression run)
• Existing test suite composition and execution history
• Risk assessment of changed areas
• Time/resource constraints for regression execution

Output Artifact

• Regression test selection recommendation
• Optimized regression suite definition
• Flaky test report and remediation plan
• Regression metrics dashboard or report

Response Format

Structured recommendation with:

1. Impact analysis summary (changed areas to affected tests mapping)
2. Selected test list with risk-based justification
3. Prioritization within the selection (critical path first)
4. Risk assessment of excluded tests (what we're accepting risk on)
5. Execution time estimate and time budget compliance

Completion Criteria

• Regression test selection produced and justified through risk analysis
• Risk coverage meets agreed threshold (excluded tests documented with rationale)
• Execution time fits within available window via prioritization
• Flaky tests identified, flagged, and quarantined
• Selection recall validated (no regression test gaps identified)

Workflow

1. Impact analysis: Map code changes to affected files, then to test suites via dependency graph and coverage data
2. Test selection: Choose tests based on risk model (business criticality, failure history, change proximity). Use dependency graph for precision
3. Prioritization: Order selected tests by risk score. Apply time budget if constrained — highest risk tests run first
4. Execution: Run selected regression suite. Monitor results in real-time. Record execution times
5. Flaky detection: Re-run failed tests up to 3 times. If inconsistent, quarantine immediately. Track flakiness rate
6. Analysis: Correlate new failures with recent changes. Distinguish real regressions from environmental issues
7. Optimization: Remove redundant tests, consolidate overlapping coverage, retire obsolete suites. Update risk scores based on results
8. Reporting: Generate pass/fail summary, risk coverage report, execution time trend, and flaky test log

Architecture / Decision Trees

Test Selection Strategy Decision Tree

How many tests are in the suite?
├── < 100 → Run all (no selection needed)
├── 100-1000 → Dependency-graph selection
│   ├── Dependency graph available? → Use graph
│   └── No graph → Coverage-based selection
├── 1000-10000 → Risk-based + dependency selection
│   ├── Historical data available? → Add failure rate to risk model
│   └── No history → Coverage + module risk only
└── 10000+ → ML-based selection
    ├── Data available? → Train selection model
    └── No data → Start with risk-based, collect data

Test Layer Decision Tree

Layer 1 (Smoke): < 5 min, always run on every deployment
  Critical path tests, auth, main workflow
Layer 2 (Core): < 30 min, run on every merge
  All core business functionality, all integration tests
Layer 3 (Full): < 2 hours, run daily
  Complete suite including edge cases
Layer 4 (Extended): 2-8 hours, run pre-release
  Full suite + performance + chaos + security

Common Pitfalls

1. Suite bloat without pruning: Regression suites accumulate dead tests. Review and remove obsolete tests quarterly
2. Running all tests on every change: Doesn't scale. Invest in test selection when suite exceeds 100 tests
3. Flaky test accumulation: Tests that fail intermittently erode trust. Quarantine within 24 hours
4. No risk model: Treating all tests as equally important misses business-critical risks
5. Ignoring test selection recall: Not tracking what was missed means you don't know your blind spots
6. Brittle test selection: Hardcoded file lists in selection logic break as codebase evolves
7. Manual regression processes: Manual regression is slow, error-prone, and doesn't scale
8. No time-budget management: Without time constraints, regression suites grow unbounded
9. Coverage over risk: 100% coverage is meaningless if it covers the wrong things
10. No feedback loop: Without analyzing missed bugs, regression can't improve

Best Practices

1. Layer the regression suite: smoke (fastest), core (balanced), full (slowest), extended (pre-release)
2. Base test selection on dependency graphs and historical failure data
3. Prioritize tests by risk score: business criticality + failure probability + change proximity
4. Quarantine flaky tests immediately and fix within one sprint
5. Add regression tests for every production bug fix (prevent re-introduction)
6. Regularly audit the regression suite: remove dead tests, consolidate duplicates
7. Use time budgets with priority ordering for constrained execution windows
8. Track regression metrics: pass rate, execution time, flakiness, risk coverage
9. Automate regression selection and execution in CI pipeline
10. Create feedback loops: analyze missed bugs and improve test coverage

Compared With

Aspect	Regression Testing	Smoke Testing	Acceptance Testing
Scope	Existing functionality	Critical path	Business requirements
Frequency	Every change + scheduled	Every deployment	Per release/story
Suite size	100-10000+ tests	5-20 tests	10-100 scenarios
Execution time	Minutes to hours	< 5 minutes	Hours to days
Selection	Risk-based + selection	Fixed suite	Per-feature planning
Focus	Regression detection	Stability check	Requirement validation
Automation	Fully automated	Fully automated	Mixed (automated + manual)

Performance Considerations

• Test selection can reduce execution time by 60-80% while maintaining 85%+ defect detection
• Dependency graph analysis: < 1 second for most codebases. Pre-compute for large monorepos
• Risk scoring: lightweight (file-level analysis) vs heavy (ML-based scoring for 10000+ tests)
• Parallel execution: N-cores speedup, but consider shared resource contention
• Flaky test re-runs: add 2x execution time for flaky detection. Reduce by quarantining flaky tests
• Time budget: allocate based on pipeline criticality. Commit stage: < 5 min. Merge stage: < 30 min

Regression Test Selection Examples

Dependency Graph-Based Selection

# .regression/dependency-graph.yml
services:
  - name: "payment-service"
    modules:
      - "src/payment/**"
      - "src/common/transactions/**"
    affected_tests:
      - "tests/payment/**"
      - "tests/integration/checkout/**"
    risk_score: 0.9
    business_criticality: "P0"
  - name: "user-service"
    modules:
      - "src/user/**"
    affected_tests:
      - "tests/user/**"
      - "tests/integration/auth/**"
    risk_score: 0.7
    business_criticality: "P1"

Risk Scoring Matrix

risk_scoring:
  factors:
    change_proximity:
      weight: 0.4
      rules:
        - "directly modified file: score 1.0"
        - "imported by modified file: score 0.5"
        - "transitively imported: score 0.2"
    failure_history:
      weight: 0.3
      rules:
        - "failed in last 10 runs: score 1.0"
        - "failed in last 50 runs: score 0.5"
        - "never failed: score 0.1"
    business_criticality:
      weight: 0.3
      rules:
        - "P0 (revenue-critical): score 1.0"
        - "P1 (important): score 0.7"
        - "P2 (standard): score 0.4"
        - "P3 (nice-to-have): score 0.1"
  thresholds:
    run_always: ">= 0.8"
    run_if_budget: ">= 0.4"
    skip: "< 0.4"

CI Integration for Regression Testing

GitHub Actions — Tiered Regression Pipeline

name: Regression Tests
on:
  pull_request:
    branches: [main]
  schedule:
    - cron: "0 2 * * *"  # Daily full regression

jobs:
  smoke-tier:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: npx playwright test --grep @smoke --timeout 30000
        env:
          CI: true
      - if: failure()
        run: echo "Smoke tests failed - blocking merge"
        # Pipeline stops here on failure

  core-tier:
    needs: smoke-tier
    runs-on: ubuntu-latest
    strategy:
      matrix:
        shard: [1, 2, 3]
    steps:
      - uses: actions/checkout@v4
      - name: Compute affected tests
        run: |
          CHANGED=$(git diff --name-only origin/main...HEAD)
          node .regression/select-tests.js --changes="$CHANGED" --tier=core
      - run: npx vitest run --shard=${{ matrix.shard }}/3 $(cat test-list.txt)
        env:
          CI: true

  full-regression:
    if: github.event_name == 'schedule'
    runs-on: ubuntu-latest
    strategy:
      matrix:
        shard: [1, 2, 3, 4, 5, 6, 7, 8]
    steps:
      - uses: actions/checkout@v4
      - run: npx vitest run --shard=${{ matrix.shard }}/8

Regression Testing Anti-Patterns

Anti-Pattern: Running Everything Every Time

Running the complete regression suite on every commit wastes CI resources and slows feedback. Once a suite exceeds 100 tests, implement test selection. Start with dependency-graph-based selection, add risk scoring as historical data accumulates.

Anti-Pattern: No Flaky Test Quarantine

Failing to quarantine flaky tests erodes trust in the entire test suite. Developers start ignoring failures, including real regressions. Quarantine flaky tests within 24 hours. Fix within one sprint or delete the test.

Anti-Pattern: Manual Regression Execution

Running regression tests manually is slow, error-prone, and doesn't scale. Every regression test must be automated. Manual testing has its place (exploratory, UAT) but regression is repetitive by definition.

Anti-Pattern: Letting Suite Bloat

Regression suites grow without bound as new features are added without removing obsolete tests. A 10,000-test suite that's never pruned has significant dead weight. Audit quarterly: remove tests for removed features, consolidate overlapping tests, retire flaky tests.

Anti-Pattern: No Time Budget Management

Without time budgets, regression suites grow unbounded. Each sprint adds tests but never removes them. Set a total suite time budget. When the budget is exceeded, prioritize: run highest risk tests first, defer low-risk tests to nightly.

Anti-Pattern: Ignoring Test Selection Recall

Using test selection without measuring recall means you don't know what you're missing (false negatives). Track bugs that escaped to production and should have been caught by regression. If recall drops below 75%, refine the selection model.

Regression Testing Maturity Model

Level	Characteristics	Practices
1: Initial	Manual regression	Testers manually re-test critical paths, no automation, no tracking
2: Defined	Automated regression suite	Full suite automated, run on every merge, basic CI integration
3: Managed	Tiered regression	Smoke/core/full tiers, risk-based selection, test selection by dependency graph
4: Measured	Optimized regression	ML-based selection, flaky test auto-quarantine, regression metrics dashboard, < 60 min full suite
5: Optimized	Predictive regression	AI-driven test selection, mutation-based selection validation, automatic test generation from production failures

Regression Metrics Dashboard

metrics:
  suite_health:
    total_tests: 4250
    active: 4120
    quarantined: 45
    flaky_rate: 1.1%
    execution_time_avg: "42 minutes"
  selection:
    avg_tests_per_pr: 340
    selection_recall: 87%
    false_negative_rate: 2.3%
    missed_bugs_last_quarter: 3
  quality_gates:
    smoke_pass_rate: 99.8%
    core_pass_rate: 97.5%
    full_pass_rate: 96.2%
    blocking_failures_per_week: 2.1

Flaky Test Management

flaky_test_workflow:
  detection:
    - "Auto-detect: test fails in CI but passes re-run (max 3 retries)"
    - "Mark as flaky: quarantine from main suite, notify owner"
    - "Track: flaky rate per test, per suite, per team"
  quarantine:
    - "Move to quarantined suite: excluded from CI gates"
    - "Assign owner: last modifier of test file"
    - "Set SLA: fix within 5 business days or delete"
  investigation:
    steps:
      - "Check test isolation: shared state, timing dependencies"
      - "Check environment: resource contention, network latency"
      - "Check data: non-deterministic fixtures, stale mocks"
      - "Check assertions: race conditions, async timing"
  resolution:
    - "Fix root cause: improve isolation, add retry logic, stabilize data"
    - "Verify: 10 consecutive runs in CI with zero failures"
    - "Re-integrate: move from quarantined back to main suite"
  metrics:
    flaky_rate_target: "< 1% of total suite"
    quarantine_age_target: "< 5 days"
    auto_fix_rate: "> 50% (common patterns: wait strategies, data isolation)"

Regression Test Data Strategy

test_data_principles:
  isolation:
    - "Each test run gets its own dataset — no sharing across runs"
    - "Use unique identifiers (timestamp + run ID) to avoid collisions"
    - "Clean up after test: delete-by-run-id in afterAll"
  factories:
    - "Use factory functions with sensible defaults and overrides"
    - "Generate unique values for constrained fields (email, username)"
    - "Never hardcode IDs — they may not exist in test environment"
  seeding:
    - "API-level seeding: POST /api/test/seed with required entities"
    - "Database-level seeding: SQL INSERT for bulk data needs"
    - "Seed in beforeAll, clean in afterAll"
  environments:
    - "CI: fresh seed per run, cleanup guaranteed"
    - "Staging: seeded data with known state, refreshed daily"
    - "Production: never use in regression tests except read-only"

Regression Testing Anti-Patterns (Additional)

Anti-Pattern: No Post-Release Validation

Running regression tests only before release and not monitoring production after deployment. The pre-release suite may pass while production has problems due to configuration differences, data volume, or real user behavior. Implement production health checks and canary analysis alongside pre-release regression.

Anti-Pattern: Skipping Security Regression

Security regression tests are skipped to save time in the deployment pipeline. Security vulnerabilities re-emerge when changes inadvertently reintroduce previously fixed issues. Security regression tests must run on every deployment regardless of change scope.

Anti-Pattern: Manual Regression Sign-Off

Relying on manual testing for regression sign-off. Manual regression is slow, error-prone, and doesn't scale beyond a few tests. Every regression test must be automated. Reserve manual testing for exploratory testing and UAT.

Rules

1. Every change must have at least a smoke test pass before regression is considered
2. Do NOT run full regression on every commit — select based on risk assessment and time budget
3. Flaky tests must be quarantined within 24 hours of detection — no false signals in main suite
4. Regression suite must complete within the available deployment window. Use prioritization if constrained
5. Test selection decisions must be documented with risk rationale — especially excluded tests
6. Coverage gaps identified during regression must be tracked and resolved within one sprint
7. Every production bug fix must include a regression test that reproduces the bug
8. Regression suite must be audited quarterly: remove dead tests, consolidate duplicates
9. Risk-based selection recall must be measured and maintained above 75%
10. Regression test execution time trend must be monitored — any increase > 20% requires investigation
11. Only deterministic tests belong in the regression suite — no tests with random or time-dependent behavior
12. Regression test failures must block deployment until root cause is identified and fixed
13. Obsolete tests (testing removed functionality) must be removed within one sprint
14. New regression tests must pass 10 consecutive runs before being added to the suite
15. Security regression tests must run even for unrelated changes (defense in depth)
16. Regression test selection must include all tests for security-critical modules regardless of change scope
17. Each regression tier must have a defined time budget and exit criteria
18. False negative rate must be tracked and maintained below 5%
19. Test data must be isolated per run — never share test data across regression runs
20. Flaky tests are quarantined within 24 hours, fixed within 5 days, or deleted

References

• references/automation-maintenance.md — Test Automation Maintenance
• references/flaky-test-management.md — Flaky Test Management
• references/regression-metrics.md — Regression Metrics
• references/regression-suites.md — Regression Suite Design
• references/regression-testing-advanced.md — Regression Testing Advanced Topics
• references/regression-testing-architecture.md — Regression Testing Architecture and System Design
• references/regression-testing-fundamentals.md — Regression Testing Fundamentals
• references/regression-testing-strategy.md — Regression Testing Strategy and Decision Frameworks
• references/risk-based-selection.md — Risk-Based Test Selection
• references/test-selection.md — Regression Test Selection

Handoff

After regression testing, hand off to:

• quality-smoke-testing — for BVT smoke suite updates on new critical paths
• quality-acceptance-testing — if acceptance criteria gaps were uncovered
• quality-e2e-testing — for end-to-end coverage of newly identified risk areas
• quality-integration-testing — for deeper investigation of regression failures

Implementation Patterns

Regression Test Suite Runner

from typing import List, Dict, Optional, Callable
from dataclasses import dataclass
from datetime import datetime
import json
import time

@dataclass
class RegressionTest:
    name: str
    run_fn: Callable
    tier: int  # 1=critical, 2=important, 3=nice-to-have
    module: str
    timeout: int = 30
    retries: int = 0

@dataclass
class TestResult:
    name: str
    passed: bool
    duration_ms: float
    error: Optional[str] = None
    retry_count: int = 0

class RegressionSuite:
    def __init__(self, name: str):
        self.name = name
        self.tests: List[RegressionTest] = []

    def add_test(self, test: RegressionTest):
        self.tests.append(test)

    def run_tier(self, tier: int, parallel: bool = False) -> Dict:
        tests = [t for t in self.tests if t.tier == tier]
        results = []
        start = time.time()

        if parallel:
            import concurrent.futures
            with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
                futures = {executor.submit(self._run_single, t): t for t in tests}
                for future in concurrent.futures.as_completed(futures):
                    results.append(future.result())
        else:
            for test in tests:
                results.append(self._run_single(test))

        duration = time.time() - start
        passed = [r for r in results if r.passed]
        failed = [r for r in results if not r.passed]

        return {
            "suite": self.name,
            "tier": tier,
            "total": len(results),
            "passed": len(passed),
            "failed": len(failed),
            "duration_seconds": round(duration, 2),
            "results": results,
        }

    def _run_single(self, test: RegressionTest) -> TestResult:
        for attempt in range(test.retries + 1):
            start = time.time()
            try:
                test.run_fn()
                duration = (time.time() - start) * 1000
                return TestResult(
                    name=test.name,
                    passed=True,
                    duration_ms=round(duration, 2),
                    retry_count=attempt,
                )
            except Exception as e:
                if attempt < test.retries:
                    continue
                duration = (time.time() - start) * 1000
                return TestResult(
                    name=test.name,
                    passed=False,
                    duration_ms=round(duration, 2),
                    error=str(e),
                    retry_count=attempt,
                )
        return TestResult(name=test.name, passed=False, duration_ms=0)

    def run_all(self, parallel: bool = False) -> Dict:
        tiers = sorted(set(t.tier for t in self.tests))
        all_results = {}
        for tier in tiers:
            all_results[tier] = self.run_tier(tier, parallel)
        return all_results


class FlakyTestDetector:
    def __init__(self, window_size: int = 10):
        self.history: Dict[str, List[bool]] = {}
        self.window_size = window_size

    def record_result(self, test_name: str, passed: bool):
        if test_name not in self.history:
            self.history[test_name] = []
        self.history[test_name].append(passed)
        if len(self.history[test_name]) > self.window_size:
            self.history[test_name].pop(0)

    def is_flaky(self, test_name: str, threshold: float = 0.2) -> bool:
        if test_name not in self.history:
            return False
        results = self.history[test_name]
        if len(results) < 5:
            return False
        pass_rate = sum(results) / len(results)
        return pass_rate < (1 - threshold) and pass_rate > threshold

Architecture Decision Trees

Regression Test Scope Selection

What changed?
├── New feature added
│   ├── Add tests for new feature
│   ├── Run full regression for affected module
│   └── Run smoke tests for entire system
│
├── Bug fix
│   ├── Add regression test for the fixed bug
│   ├── Run all tests in the affected module
│   └── Run integration tests for dependent modules
│
├── Refactoring (no behavior change)
│   ├── Run full test suite for refactored module
│   ├── Contract tests for API changes
│   └── Compare before/after test coverage
│
├── Dependency update
│   ├── Run full regression suite
│   ├── Run security-focused tests
│   └── Test edge cases in updated dependency
│
└── Configuration / infrastructure change
    ├── Smoke tests for deployment
    ├── Health check tests
    └── Performance benchmark comparison

Anti-Patterns

Anti-Pattern	Why It Fails	Correct Approach
No tiered regression	Full suite too slow for CI feedback	Tier 1 (critical) in 5 min, Tier 2 in 15 min, Tier 3 nightly
Ignoring flaky tests	Wasted CI time, distrust in suite	Quarantine flaky tests immediately, fix within 5 days
Only adding, never removing	Suite grows unbounded, slow	Remove tests for deleted features, review suite quarterly
Determinism not enforced	Random failures from shared state	Isolate test data per run, no shared state
No baseline for comparison	Don't know if performance regressed	Store baseline metrics, compare on each run

Performance Optimization

• Impact analysis for test selection: Use code coverage data to select tests for changed files. Only run tests that cover modified code. Reduces regression run time by 60-80%.
• Parallel test execution by tier: Run Tier 1 tests sequentially for fast feedback. Run Tier 2+3 tests in parallel across multiple machines. Total regression time: Tier 1 in 5 min, all tiers in 30 min.
• Test impact analysis: Use git diff to identify changed files. Map files to test suites. Only run affected suites. Cache unaffected suite results from previous CI run.