[Backport 7.55.x] [FA] Mark apm inject as flaky

[agent-platform-auto-pr]

Backport 18af930 from #27215.

---

What does this PR do?

Tag fleet apm inject test as flaky during investigation

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[pr-commenter]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=40872198 --os-family=ubuntu

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 44.93%. Comparing base (89afbb0) to head (bc5c7a5).

Additional details and impacted files

@@            Coverage Diff             @@
##           7.55.x   #27703      +/-   ##
==========================================
- Coverage   44.94%   44.93%   -0.01%     
==========================================
  Files        2354     2354              
  Lines      272910   272910              
==========================================
- Hits       122665   122640      -25     
- Misses     140570   140593      +23     
- Partials     9675     9677       +2     

Flag	Coverage Δ
amzn_aarch64	45.77% <ø> (-0.02%)	⬇️
centos_x86_64	45.69% <ø> (-0.01%)	⬇️
ubuntu_aarch64	45.78% <ø> (-0.01%)	⬇️
ubuntu_x86_64	45.77% <ø> (-0.01%)	⬇️
windows_amd64	50.76% <ø> (-0.02%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 394af54f-216c-4bbc-b2ed-75d93acb7d48

Baseline: 89afbb0
Comparison: bc5c7a5

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	links
➖	pycheck_1000_100byte_tags	% cpu utilization	+1.94	[-3.00, +6.88]
➖	basic_py_check	% cpu utilization	+1.37	[-1.23, +3.98]
➖	idle	memory utilization	+0.33	[+0.29, +0.36]
➖	otel_to_otel_logs	ingress throughput	+0.11	[-0.25, +0.47]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.05	[+0.01, +0.08]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.20, +0.20]
➖	trace_agent_msgpack	ingress throughput	-0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.01	[-0.03, +0.01]
➖	file_tree	memory utilization	-0.21	[-0.29, -0.13]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.14	[-3.99, +1.71]
➖	tcp_syslog_to_blackhole	ingress throughput	-7.76	[-28.32, +12.80]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[chouetz]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in 7.55.x is 36m.

Use /merge -c to cancel this operation!