2nd/10_Wiki/Topics/Domain_Programming/From_Topics_Root/Green-Check-Mark-Syndrome.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-green-check-mark-syndrome	Green Check Mark Syndrome	10_Wiki/Topics	verified	self	Green Check Syndrome CI Theater Test Theater	none	A	0.9	applied	anti-pattern ci-cd testing observability		2026-05-10	pending	language framework TypeScript/Python GitHub-Actions/pytest

Green Check Mark Syndrome

매 한 줄

"매 green CI ≠ correct system." 매 anti-pattern: 매 passing tests 의 confidence inflation, 매 actual coverage / assertion strength / production behavior 의 disconnect. 매 2026 prevalent — 매 LLM-generated tests, 매 mock-heavy suites, 매 flaky-retry hides 매 real bugs.

매 핵심

매 Symptoms

• 매 100% green builds, 매 still production incidents 의 frequent.
• 매 tests assert truisms (expect(1).toBe(1)).
• 매 mocks return canned data — 매 integration paths 의 untested.
• 매 retries hide flakiness — 매 race conditions 의 ignored.
• 매 coverage % high, 매 mutation score low.

매 Root causes

• Goodhart's law: 매 green check 의 metric → metric 의 target → 매 gamed.
• Mock theater: 매 unit isolation 의 over-mocked, 매 real failure modes 의 missed.
• AI-generated tests: 매 LLM이 매 implementation을 매 mirror — 매 same bug 의 test에도 present.
• Flaky-retry culture: 매 "retry until green" 의 normalized.

매 Detection

• Mutation testing — 매 assertion strength measurement.
• Property-based testing — 매 input space coverage.
• Production observability — 매 errors in prod that tests 의 missed.
• Test-impact analysis — 매 untouched code paths surface.

매 응용

1. CI quality dashboard — 매 mutation score + flake rate.
2. Test review checklist — 매 each test 의 specific failure mode가 매 catch?
3. Chaos engineering — 매 production-like failures inject.

💻 패턴

Mutation testing (Stryker)

// stryker.conf.json
{
  "mutate": ["src/**/*.ts"],
  "testRunner": "vitest",
  "thresholds": { "high": 80, "low": 60, "break": 50 }
}

npx stryker run
# 매 surviving mutants 의 매 weak tests indicate

Property-based test

import { fc } from 'fast-check';

test('reverse twice = identity', () => {
  fc.assert(
    fc.property(fc.array(fc.integer()), (arr) => {
      expect(reverse(reverse(arr))).toEqual(arr);
    })
  );
});

Flake-detector (no silent retry)

# .github/workflows/test.yml
- name: Test
  run: npm test
  # NO retry — 매 flake 의 immediately surface
- name: Flake report
  if: failure()
  run: |
    echo "::warning::Test failed — investigate, do not retry blindly"

Assertion-strength linter

# detect weak assertions
import ast
from pathlib import Path

WEAK_PATTERNS = {'assertTrue(True)', 'assertEqual(1, 1)', 'assert True'}

for file in Path('tests').rglob('*.py'):
    tree = ast.parse(file.read_text())
    for node in ast.walk(tree):
        if isinstance(node, ast.Call) and ast.unparse(node) in WEAK_PATTERNS:
            print(f"WEAK: {file}:{node.lineno}")

Production-trace replay

# 매 prod traces → test fixtures
import json
from opentelemetry.trace import get_tracer

def replay_prod_trace(trace_id: str):
    trace = fetch_trace(trace_id)  # from observability backend
    inputs = extract_inputs(trace)
    result = run_system(inputs)
    expected = trace.outputs
    assert result == expected, f"Drift from prod: {trace_id}"

Real integration (no mocks)

// testcontainers — 매 real DB
import { GenericContainer } from 'testcontainers';

let pg: StartedTestContainer;
beforeAll(async () => {
  pg = await new GenericContainer('postgres:17')
    .withExposedPorts(5432)
    .withEnvironment({ POSTGRES_PASSWORD: 'test' })
    .start();
});

test('real query', async () => {
  const client = connect(pg.getMappedPort(5432));
  await client.query('CREATE TABLE u (id int)');
  // 매 real SQL behavior tested
});

매 결정 기준

상황	Approach
New test suite	Property-based + integration
Existing green-but-fragile suite	Mutation testing audit
Flaky test	Investigate root cause, never blind-retry
Mock-heavy suite	Add testcontainers for real I/O
Coverage-driven culture	Switch metric to mutation score

기본값: 매 green CI 의 trust 의 X — 매 mutation score + 매 prod observability + 매 chaos drills 의 combined signal.

🔗 Graph

• 부모: CI CD
• 변형: Test-Theater
• 응용: Mutation-Testing · Property-Based-Testing · Chaos-Engineering
• Adjacent: Goodharts-Law · Observability

🤖 LLM 활용

언제: test review (assertion strength critique), mutation report triage, prod-trace replay generation. 언제 X: 매 LLM이 매 test generation 단독 — 매 same blind spots reproduce.

❌ 안티패턴

• Coverage as quality: 매 100% line coverage, 매 0% mutation kill rate.
• Auto-retry on fail: 매 race condition 의 hide → prod incident.
• Mock everything: 매 unit "passes", 매 integration broken.
• LLM-only test suite: 매 implementation mirror — 매 bug parity.

🧪 검증 / 중복

• Verified (Hillel Wayne "Test theater", Google Testing Blog "Just Say No to More End-to-End Tests").
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — CI theater anti-pattern, mutation testing, real integration