Billing
Pend 0Skip 0Runs 0
0
0.00%
0.00%
324
Executive Snapshot
3
Daily Runs
2/3
Daily Green
22m 50s
Avg Daily Runtime
8
Smoke Attempts
3/8
Smoke Green
3m 38s
Avg Smoke Runtime
3m 39s
Median Smoke Time
1
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 2 of 3 runs (66.7%), with a current green streak of 1 and a best streak of 1 in this window.
- Smoke passed 3 of 8 attempts (37.5%) across 5 production pipelines.
- Failure concentration is not random: Frontend has the highest strict failure ratio at 0.12%, while Frontend has the broadest non-pass footprint at 0.12%.
- University is the weakest smoke surface in this window at 1/3 green (33.3%).
- Daily-suite runtime averaged 22m 50s.
Engineering Analysis
- The failure profile is concentrated enough to act on. Frontend and Frontend are carrying the strongest signal, which means reliability work should be assigned by category ownership instead of treating the suite as one undifferentiated problem.
- Smoke is lagging the broader regression suite, so deploy readiness is probably being constrained more by environment/setup stability and narrow critical-path checks than by overall test volume.
Recommended Actions
- Assign one owner to Frontend 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.
Billing0.00%
Web0.00%
Frontend0.12%
Library0.00%
Non-pass Ratio
Share of category executions that ended in failed, pending, or skipped across all daily runs in this window.
Billing0.00%
Web0.00%
Frontend0.12%
Library0.00%
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 | Pending | Skipped | Failure Ratio | Non-pass Ratio | Runs With Failures |
|---|---|---|---|---|---|---|---|
| Billing | 324 | 0 | 0 | 0 | 0.00% | 0.00% | 0 |
| Web | 2340 | 0 | 0 | 0 | 0.00% | 0.00% | 0 |
| Frontend | 810 | 1 | 0 | 0 | 0.12% | 0.12% | 1 |
| Library | 258 | 0 | 0 | 0 | 0.00% | 0.00% | 0 |
CatFailF%NP%Tot
Web
Pend 0Skip 0Runs 0
0
0.00%
0.00%
2340
Frontend
Pend 0Skip 0Runs 1
1
0.12%
0.12%
810
Library
Pend 0Skip 0Runs 0
0
0.00%
0.00%
258
Recent Runs
Recent Daily Suite Runs
Recent Smoke Attempts
| Date | Suite | Pipeline | Job | Status | Passed | Failed | Duration |
|---|---|---|---|---|---|---|---|
| 2026-05-05 16:13 | University | 154657 | University smoke | FAILED | 57 | 3 | 3m 54s |
| 2026-05-05 16:16 | Frontend | 154657 | Frontend smoke | FAILED | 109 | 1 | 3m 32s |
| 2026-05-05 17:09 | University | 154676 | University smoke | FAILED | 57 | 3 | 3m 43s |
| 2026-05-05 17:12 | Frontend | 154676 | Frontend smoke | FAILED | 109 | 1 | 3m 51s |
| 2026-05-05 18:29 | Frontend | 154686 | Frontend smoke | FAILED | 109 | 1 | 3m 29s |
| 2026-05-05 19:34 | Frontend | 154691 | Frontend smoke | PASSED | 110 | 0 | 3m 06s |
| 2026-05-07 15:31 | University | 155030 | University smoke | PASSED | 60 | 0 | 3m 39s |
| 2026-05-07 15:34 | Frontend | 155030 | Frontend smoke | PASSED | 110 | 0 | 3m 47s |
FAILED
P 57 | F 3 | 3m 54s
FAILED
P 109 | F 1 | 3m 32s
FAILED
P 57 | F 3 | 3m 43s
FAILED
P 109 | F 1 | 3m 51s
FAILED
P 109 | F 1 | 3m 29s
PASSED
P 110 | F 0 | 3m 06s
PASSED
P 60 | F 0 | 3m 39s
PASSED
P 110 | F 0 | 3m 47s
Smoke Suite Breakdown
Frontend
5 attempts across 5 pipelines
Passed2
Failed3
Incomplete0
Avg runtime3m 33s
Median passing runtime3m 26s
Pipelines5
University
3 attempts across 3 pipelines
Passed1
Failed2
Incomplete0
Avg runtime3m 45s
Median passing runtime3m 39s
Pipelines3