Billing
Pend 0Skip 0Runs 1
1
0.12%
0.12%
864
Executive Snapshot
8
Daily Runs
1/8
Daily Green
331m 02s
Avg Daily Runtime
4
Smoke Attempts
4/4
Smoke Green
3m 05s
Avg Smoke Runtime
3m 04s
Median Smoke Time
0
Current Green Streak
Executive Analysis
Bottom line: the regression system is informative but not calm. The data suggest repeatable problem areas rather than random breakage, which means focused ownership should move the needle quickly.
What Matters
- Daily regression passed 1 of 8 runs (12.5%), with a current green streak of 0 and a best streak of 1 in this window. The latest daily run (152618) failed, so the system is ending the week under tension rather than in a clean state. 3 failed run(s) never reached complete daily-suite counts, which points to some infrastructure or setup noise mixed into the product signal.
- Smoke passed 4 of 4 attempts (100.0%) across 2 production pipelines.
- Failure concentration is not random: Library has the highest strict failure ratio at 0.69%, while Library has the broadest non-pass footprint at 0.69%.
- Frontend is the weakest smoke surface in this window at 2/2 green (100.0%).
- Daily-suite runtime averaged 331m 02s, while observed daily test volume moved from 1,159 to 1,244.
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. Library and Library 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.
- The daily suite is now large enough that runtime itself is becoming a management variable at 331m 02s average duration. At that size, every additional flaky or redundant test has a measurable cost on feedback speed.
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 Library 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 Frontend 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.
Billing0.12%
Web0.14%
Frontend0.60%
Library0.69%
Non-pass Ratio
Share of category executions that ended in failed, pending, or skipped across all daily runs in this window.
Billing0.12%
Web0.14%
Frontend0.60%
Library0.69%
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 | 864 | 1 | 0 | 0 | 0.12% | 0.12% | 1 |
| Web | 6240 | 9 | 0 | 0 | 0.14% | 0.14% | 3 |
| Frontend | 2160 | 13 | 0 | 0 | 0.60% | 0.60% | 7 |
| Library | 433 | 3 | 0 | 0 | 0.69% | 0.69% | 3 |
CatFailF%NP%Tot
Web
Pend 0Skip 0Runs 3
9
0.14%
0.14%
6240
Frontend
Pend 0Skip 0Runs 7
13
0.60%
0.60%
2160
Library
Pend 0Skip 0Runs 3
3
0.69%
0.69%
433
Recent Runs
Recent Daily Suite Runs
| Date | Pipeline | Suites | Status | Summary |
|---|---|---|---|---|
| 2026-04-11 18:25 | 152054 | BillingWebFrontendLibrary | FAILED | Total 1159 | Passed 1157 | Failed 2 | Incomplete suite counts |
| 2026-04-13 14:34 | 152056 | BillingWebFrontendLibrary | FAILED | Total 1159 | Passed 1157 | Failed 2 | Incomplete suite counts |
| 2026-04-14 14:42 | 152058 | BillingWebFrontendLibrary | FAILED | Total 1159 | Passed 1155 | Failed 4 | Incomplete suite counts |
| 2026-04-14 18:25 | 152092 | BillingWebFrontendLibrary | FAILED | Total 1244 | Passed 1243 | Failed 1 |
| 2026-04-15 18:24 | 152229 | BillingWebFrontendLibrary | PASSED | Total 1244 | Passed 1244 | Failed 0 |
| 2026-04-16 19:10 | 152398 | BillingWebFrontendLibrary | FAILED | Total 1244 | Passed 1239 | Failed 5 |
| 2026-04-17 18:25 | 152610 | BillingWebFrontendLibrary | FAILED | Total 1244 | Passed 1237 | Failed 7 |
| 2026-04-18 18:25 | 152618 | BillingWebFrontendLibrary | FAILED | Total 1244 | Passed 1239 | Failed 5 |
FAILED
T 1159 | P 1157 | F 2 | Pend 0 | Incomplete
FAILED
T 1159 | P 1157 | F 2 | Pend 0 | Incomplete
FAILED
T 1159 | P 1155 | F 4 | Pend 0 | Incomplete
FAILED
T 1244 | P 1243 | F 1 | Pend 0
PASSED
T 1244 | P 1244 | F 0 | Pend 0
FAILED
T 1244 | P 1239 | F 5 | Pend 0
FAILED
T 1244 | P 1237 | F 7 | Pend 0
FAILED
T 1244 | P 1239 | F 5 | Pend 0
Recent Smoke Attempts
| Date | Suite | Pipeline | Job | Status | Passed | Failed | Duration |
|---|---|---|---|---|---|---|---|
| 2026-04-15 16:56 | University | 152212 | University smoke | PASSED | 10 | 0 | 2m 47s |
| 2026-04-15 17:00 | Frontend | 152212 | Frontend smoke | PASSED | 110 | 0 | 3m 16s |
| 2026-04-17 13:12 | University | 152536 | University smoke | PASSED | 10 | 0 | 2m 53s |
| 2026-04-17 13:16 | Frontend | 152536 | Frontend smoke | PASSED | 110 | 0 | 3m 22s |
PASSED
P 10 | F 0 | 2m 47s
PASSED
P 110 | F 0 | 3m 16s
PASSED
P 10 | F 0 | 2m 53s
PASSED
P 110 | F 0 | 3m 22s
Smoke Suite Breakdown
Frontend
2 attempts across 2 pipelines
Passed2
Failed0
Incomplete0
Avg runtime3m 19s
Median passing runtime3m 19s
Pipelines2
University
2 attempts across 2 pipelines
Passed2
Failed0
Incomplete0
Avg runtime2m 50s
Median passing runtime2m 50s
Pipelines2