Engineering Council Test Reliability Report

Executive Snapshot

9

Daily Runs

0/9

Daily Green

9m 19s

Avg Daily Runtime

19

Smoke Attempts

15/19

Smoke Green

3m 32s

Avg Smoke Runtime

4m 33s

Median Smoke Time

0

Current Green Streak

Executive Analysis

Bottom line: release confidence is unstable in both the broad regression path and the deploy smoke path. The immediate job is to separate real product regressions from execution noise, then burn down the concentrated failure clusters.

What Matters

Daily regression passed 0 of 9 runs (0.0%), with a current green streak of 0 and a best streak of 0 in this window. The latest daily run (159156) failed, so the system is ending the week under tension rather than in a clean state. 9 failed run(s) never reached complete daily-suite counts, which points to some infrastructure or setup noise mixed into the product signal.
Smoke passed 15 of 19 attempts (78.9%) across 11 production pipelines. 1 pipeline(s) recovered on rerun, which is useful for continuity but also a sign that first-pass deploy signal is noisier than it should be. 1 failed attempt(s) never reached test execution counts at all.
Failure concentration is not random: Billing has the highest strict failure ratio at 1.03%, while Billing has the broadest non-pass footprint at 11.11%.
University is the weakest smoke surface in this window at 5/8 green (62.5%).
Daily-suite runtime averaged 9m 19s.

Engineering Analysis

A release gate should fail loudly for product regressions and quietly for infrastructure noise. Rerun recoveries plus incomplete daily or smoke attempts suggest those two failure modes are still partially mixed together.
The failure profile is concentrated enough to act on. Billing and Billing are carrying the strongest signal, which means reliability work should be assigned by category ownership instead of treating the suite as one undifferentiated problem.
The broader daily suite is carrying more instability than smoke, which usually means product regressions are escaping into wider coverage areas even when the narrow deploy gate looks acceptable.

Recommended Actions

Split incomplete execution failures from real assertion failures in the report narrative. Setup breakage should stay visible, but it should not look identical to a product regression in the executive readout.
Assign one owner to Billing for the next cycle and expect a short written burn-down: top failing tests, suspected root causes, flake versus regression breakdown, and what gets fixed or quarantined first.
Treat the daily regression suite like an operations queue until it is calm again: triage failures after each red run, close known-noise items fast, and avoid letting multiple unrelated red signals pile up between runs.
Put University smoke under closer guardrails for the next release cycle. It is the best place to improve first-pass deploy confidence quickly.

Improvement Ideas

Introduce a small reliability budget for tests: every flaky or quarantined case needs an owner and an expiry, and the team should review that budget weekly the same way it reviews bugs or incidents.
Track first-fail to root-cause time as a core metric. Fast diagnosis is as important as raw pass rate because the practical value of a test gate depends on how quickly it helps the team recover.
Define a runtime budget per suite and require justification when test count or duration grows. Reliable feedback systems stay trusted when they remain both stable and proportionate.

Category Execution Ratios

How computed

Category total executions means the sum of that category's observed test executions across every daily-suite run in the selected window.

Strict Failure Ratio = failed executions for that category divided by total executions for that category across the window.

Non-pass Ratio = (failed + pending + skipped) executions for that category divided by total executions for that category across the window.

Example: if Billing executed 800 times across the week and 2 of those executions failed, Billing strict failure ratio is 0.25%. That does not mean 0.25% of pipelines failed; it means 0.25% of observed Billing executions ended in failed.

How computed

Category total executions means the sum of that category's observed test executions across every daily-suite run in the selected window.

Strict Failure Ratio = failed executions for that category divided by total executions for that category across the window.

Non-pass Ratio = (failed + pending + skipped) executions for that category divided by total executions for that category across the window.

Example: if Billing executed 800 times across the week and 2 of those executions failed, Billing strict failure ratio is 0.25%. That does not mean 0.25% of pipelines failed; it means 0.25% of observed Billing executions ended in failed.

Strict Failure Ratio

Share of category executions that ended in failed across all daily runs in this window.

Billing1.03%

Web0.00%

Frontend0.00%

Library0.90%

Non-pass Ratio

Share of category executions that ended in failed, pending, or skipped across all daily runs in this window.

Billing11.11%

Web0.00%

Frontend0.00%

Library3.10%

Category Aggregate Table

How computed

Category total executions means the sum of that category's observed test executions across every daily-suite run in the selected window.

Strict Failure Ratio = failed executions for that category divided by total executions for that category across the window.

Non-pass Ratio = (failed + pending + skipped) executions for that category divided by total executions for that category across the window.

Example: if Billing executed 800 times across the week and 2 of those executions failed, Billing strict failure ratio is 0.25%. That does not mean 0.25% of pipelines failed; it means 0.25% of observed Billing executions ended in failed.

How computed

Category total executions means the sum of that category's observed test executions across every daily-suite run in the selected window.

Strict Failure Ratio = failed executions for that category divided by total executions for that category across the window.

Non-pass Ratio = (failed + pending + skipped) executions for that category divided by total executions for that category across the window.

Example: if Billing executed 800 times across the week and 2 of those executions failed, Billing strict failure ratio is 0.25%. That does not mean 0.25% of pipelines failed; it means 0.25% of observed Billing executions ended in failed.

Category	Total	Failed	Skipped	Failure Ratio	Non-pass Ratio	Runs With Failures
Billing	972	10	98	1.03%	11.11%	1
Web	0	0	0	0.00%	0.00%	9
Frontend	0	0	0	0.00%	0.00%	9
Library	774	7	17	0.90%	3.10%	1

Billing

Pend 0Skip 98Runs 1

10

1.03%

11.11%

972

Web

Pend 0Skip 0Runs 9

0

0.00%

0

Frontend

Pend 0Skip 0Runs 9

0

0.00%

0

Library

Pend 0Skip 17Runs 1

7

0.90%

3.10%

774

Recent Runs