e2e-suite-budget

Overview

E2E tests are expensive: per test-pyramid (Cohn), they're "brittle, expensive to write, and time consuming to run." Without active management, the E2E suite grows: a new feature adds 5 tests per sprint; flake rate creeps up; CI time balloons; team disables flaky tests; coverage illusory.

This skill computes per-test ROI and recommends which tests to retire / move to a lower layer / fix.

When to use

• E2E suite has grown past 50-100 tests.
• CI time is dominated by E2E.
• Flake rate >5%; team is fatigued.
• Quarterly: scheduled budget review.

Step 1 - Inputs

Per-E2E-test, the agent / skill needs:

• Runtime: average per-test duration (from CI).
• Flake rate: % of CI runs where the test failed then passed on retry (from CI history per junit-xml-analysis Step 3).
• Regression-catch count: how many real bugs this test caught in the last N months (from incident postmortems referencing the test, or git-blame on the failing-then-fixed pattern).
• Maintenance cost: PR count modifying the test in last N months (proxy for fragility).
• Value tier: 1-5; assigned by team (5 = critical journey).

Step 2 - ROI formula

ROI = (regressions_caught × value_tier) / (runtime_min × (1 + flake_rate) × (1 + maintenance_count_norm))

Where:

• regressions_caught ≥ 0 (defaults to 0; can be fractional for "caught a near-miss").
• value_tier ∈ [1, 5].
• runtime_min is the test's average runtime in minutes.
• flake_rate ∈ [0, 1] (e.g., 0.05 = 5% flake).
• maintenance_count_norm is PRs touching the test in the window divided by the median for the suite (so a typical-maintenance test has factor ~1).

Higher ROI = more value per cost.

Step 3 - Per-test scoring

# scripts/e2e-budget.py
import json, sys
from collections import defaultdict

# Load per-test stats from CI history
stats = json.load(open(sys.argv[1]))  # {test_id: {runtime, flake_rate, ...}}
regressions = json.load(open(sys.argv[2]))  # {test_id: count}
value_tiers = json.load(open(sys.argv[3]))  # {test_id: tier}
maintenance = json.load(open(sys.argv[4]))  # {test_id: pr_count}
median_pr_count = sorted(maintenance.values())[len(maintenance) // 2] or 1

scores = {}
for tid, s in stats.items():
    rc = regressions.get(tid, 0)
    vt = value_tiers.get(tid, 3)
    rt = s['runtime_min']
    fr = s['flake_rate']
    mn = maintenance.get(tid, 0) / median_pr_count
    score = (rc * vt) / (rt * (1 + fr) * (1 + mn))
    scores[tid] = score

# Sort ascending — lowest ROI first
ranked = sorted(scores.items(), key=lambda x: x[1])
print(json.dumps(ranked))

Step 4 - Output: bottom decile

## E2E suite budget — `<repo>` — Q2 2026

**Total E2E tests:** 142
**Total runtime:** 38 min (per CI run)
**Median flake rate:** 4.2%
**Bottom-decile (14 tests) recommended for action:**

| Test                                              | ROI | Runtime | Flake | Regressions caught | Value tier | Recommendation |
|---------------------------------------------------|----:|--------:|------:|-------------------:|-----------:|----------------|
| `archive-flow.spec.ts > old-orders`                | 0.0 |   2.1m  |  18%  |        0           |     2      | Retire — high flake, no signal in 6mo. |
| `legacy-checkout.spec.ts > deprecated-promo`       | 0.1 |   3.2m  |   8%  |        0           |     1      | Retire — feature deprecated. |
| `cart.spec.ts > add 1000 items`                    | 0.2 |   4.5m  |   2%  |        0           |     2      | Move to perf suite — not E2E concern. |
| `e2e-utils.spec.ts > date-formatting`              | 0.5 |   0.8m  |   1%  |        0           |     2      | Move to unit layer. |
| ... (10 more)                                       |     |         |       |                    |            |                |

### Estimated impact of acting on all 14

- Suite size: 142 → 128 (-14)
- Runtime: 38 min → 28 min (-10 min per CI run, ~26% reduction)
- Flake-related reruns: estimated -50%
- Maintenance load: -20% (these 14 had the highest PR-touch count)

Step 5 - Categorize recommendations

The team picks the appropriate class per test; the skill recommends.

Step 6 - Cap discipline

Set an absolute budget:

# e2e-budget.yml
budget:
  max_tests: 100
  max_runtime_min: 30
  max_flake_rate: 0.03   # 3%

When the suite exceeds budget, the next sprint's "add new E2E test" requires retiring / moving an existing one. Force the trade-off.

Step 7 - Cadence

Anti-patterns

Limitations

• Regressions-caught data is hard to gather. Without postmortem cross-references, defaults to 0 for all tests; ranking gets degraded.
• ROI formula is heuristic. Tune weights per team's priorities.
• Doesn't account for "test of last resort" - some tests have low historical catch rate but exist because regression there would be catastrophic (auth, payment).
• Migration cost. Moving an E2E test to a unit test isn't free; budget the engineering time.

References

• tp - Cohn's pyramid: UI tests "brittle, expensive to write, and time consuming to run."
• test-pyramid-balancer - sibling: identifies the layer-balance issue this skill addresses tactically.
• flaky-test-quarantine - sibling: handles the flake side of low-ROI tests.
• junit-xml-analysis - upstream: provides per-test runtime + flake stats.
• unit-test-coverage-targeter - complementary: identifies what to add at the unit layer when E2E tests get retired.