Billing
Pend 0Skip 60Runs 6
31
3.03%
8.90%
1022
Executive Snapshot
7
Daily Runs
0/7
Daily Green
16m 59s
Avg Daily Runtime
7
Smoke Attempts
6/7
Smoke Green
5m 43s
Avg Smoke Runtime
4m 19s
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 0 of 7 runs (0.0%), with a current green streak of 0 and a best streak of 0 in this window. The latest daily run (167452) failed, so the system is ending the week under tension rather than in a clean state. 7 failed run(s) never reached complete daily-suite counts, which points to some infrastructure or setup noise mixed into the product signal.
- Smoke passed 6 of 7 attempts (85.7%) across 4 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.
- Failure concentration is not random: Billing has the highest strict failure ratio at 3.03%, while Billing has the broadest non-pass footprint at 8.90%.
- University is the weakest smoke surface in this window at 2/3 green (66.7%).
- Daily-suite runtime averaged 16m 59s.
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.
Billing3.03%
Web0.00%
Frontend0.00%
Library0.50%
Non-pass Ratio
Share of category executions that ended in failed, pending, or skipped across all daily runs in this window.
Billing8.90%
Web0.00%
Frontend0.00%
Library1.66%
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 | 1022 | 31 | 0 | 60 | 3.03% | 8.90% | 6 |
| Web | 0 | 0 | 0 | 0 | 0.00% | 0.00% | 7 |
| Frontend | 0 | 0 | 0 | 0 | 0.00% | 0.00% | 7 |
| Library | 602 | 3 | 0 | 7 | 0.50% | 1.66% | 2 |
CatFailF%NP%Tot
Web
Pend 0Skip 0Runs 7
0
0.00%
0.00%
0
Frontend
Pend 0Skip 0Runs 7
0
0.00%
0.00%
0
Library
Pend 0Skip 7Runs 2
3
0.50%
1.66%
602
Recent Runs
Recent Daily Suite Runs
| Date | Pipeline | Suites | Status | Summary |
|---|---|---|---|---|
| 2026-08-15 18:20 | 166804 | BillingWebFrontendLibrary | FAILED | Total 232 | Passed 227 | Failed 5 | Incomplete suite counts |
| 2026-08-16 18:18 | 166806 | BillingWebFrontendLibrary | FAILED | Total 232 | Passed 228 | Failed 4 | Incomplete suite counts |
| 2026-08-17 18:18 | 166913 | BillingWebFrontendLibrary | FAILED | Total 232 | Passed 160 | Failed 11 | Incomplete suite counts |
| 2026-08-18 18:20 | 167033 | BillingWebFrontendLibrary | FAILED | Total 232 | Passed 227 | Failed 5 | Incomplete suite counts |
| 2026-08-19 18:23 | 167166 | BillingWebFrontendLibrary | FAILED | Total 232 | Passed 219 | Failed 7 | Incomplete suite counts |
| 2026-08-20 18:21 | 167326 | BillingWebFrontendLibrary | FAILED | Total 232 | Passed 230 | Failed 2 | Incomplete suite counts |
| 2026-08-21 18:19 | 167452 | BillingWebFrontendLibrary | FAILED | Total 232 | Passed 232 | Failed 0 | Incomplete suite counts |
FAILED
T 232 | P 227 | F 5 | Pend 0 | Incomplete
FAILED
T 232 | P 228 | F 4 | Pend 0 | Incomplete
FAILED
T 232 | P 160 | F 11 | Pend 0 | Incomplete
FAILED
T 232 | P 227 | F 5 | Pend 0 | Incomplete
FAILED
T 232 | P 219 | F 7 | Pend 0 | Incomplete
FAILED
T 232 | P 230 | F 2 | Pend 0 | Incomplete
FAILED
T 232 | P 232 | F 0 | Pend 0 | Incomplete
Recent Smoke Attempts
| Date | Suite | Pipeline | Job | Status | Passed | Failed | Duration |
|---|---|---|---|---|---|---|---|
| 2026-08-17 13:46 | Frontend | 166821 | Frontend smoke | PASSED | 110 | 0 | 4m 18s |
| 2026-08-17 15:53 | Frontend | 166889 | Frontend smoke | PASSED | 110 | 0 | 4m 25s |
| 2026-08-17 16:02 | University | 166889 | University smoke | FAILED | 26 | 34 | 14m 32s |
| 2026-08-20 17:23 | Frontend | 167319 | Frontend smoke | PASSED | 110 | 0 | 4m 19s |
| 2026-08-20 17:34 | University | 167319 | University smoke | PASSED | 60 | 0 | 3m 40s |
| 2026-08-21 15:33 | University | 167442 | University smoke | PASSED | 60 | 0 | 3m 56s |
| 2026-08-21 15:59 | Frontend | 167442 | Frontend smoke | PASSED | 110 | 0 | 4m 49s |
PASSED
P 110 | F 0 | 4m 18s
PASSED
P 110 | F 0 | 4m 25s
FAILED
P 26 | F 34 | 14m 32s
PASSED
P 110 | F 0 | 4m 19s
PASSED
P 60 | F 0 | 3m 40s
PASSED
P 60 | F 0 | 3m 56s
PASSED
P 110 | F 0 | 4m 49s
Smoke Suite Breakdown
Frontend
4 attempts across 4 pipelines
Passed4
Failed0
Incomplete0
Avg runtime4m 28s
Median passing runtime4m 22s
Pipelines4
University
3 attempts across 3 pipelines
Passed2
Failed1
Incomplete0
Avg runtime7m 23s
Median passing runtime3m 48s
Pipelines3