remove stale unit test

[adel121]

What does this PR do?

See title.

Motivation

The removed unit test was testing the function BuildTaggerEntityName which is now removed, so no need for the unit test anymore.

Describe how to test/QA your changes

No need for qa.

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

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=46364639 --os-family=ubuntu

Note: This applies to commit 31fd2a8

[adel121]

/merge

[dd-devflow]

🚂 MergeQueue: waiting for PR to be ready

This merge request is not mergeable yet, because of pending checks/missing approvals. It will be added to the queue as soon as checks pass and/or get approvals.
Note: if you pushed new commits since the last approval, you may need additional approval.
You can remove it from the waiting list with /remove command.

Use /merge -c to cancel this operation!

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 26m.

Use /merge -c to cancel this operation!

[dd-devflow]

❌ MergeQueue: The build pipeline contains failing jobs for this merge request

Build pipeline has failing jobs for 2d502e7:

• tests_flavor_dogstatsd_deb-x64

⚠️ Do NOT retry failed jobs directly (why?).

What to do next?

• Investigate the failures and when ready, re-add your pull request to the queue!
• Any question, go check the FAQ.

Details

Since those jobs are not marked as being allowed to fail, the pipeline will most likely fail.
Therefore, and to allow other builds to be processed, this merge request has been rejected and the pipeline got canceled.

If you need support, contact us on Slack #devflow with those details!

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: a5f0e553-f392-408a-813e-39a3503b8bdb Metrics dashboard Target profiles

Baseline: 967da85
Comparison: 31fd2a8

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	trials	links
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.41	[+0.67, +2.14]	1	Logs
➖	basic_py_check	% cpu utilization	+1.07	[-1.69, +3.83]	1	Logs
➖	file_tree	memory utilization	+0.50	[+0.37, +0.63]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.31	[-0.51, +1.13]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.13	[-0.36, +0.63]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.12	[-0.13, +0.36]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.11	[-0.11, +0.34]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.09	[-0.08, +0.27]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.10, +0.11]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.01	[-0.34, +0.32]	1	Logs
➖	idle	memory utilization	-0.04	[-0.09, -0.00]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.06	[-0.10, -0.01]	1	Logs
➖	idle_all_features	memory utilization	-0.23	[-0.32, -0.14]	1	Logs bounds checks dashboard
➖	pycheck_lots_of_tags	% cpu utilization	-1.76	[-4.22, +0.70]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
❌	file_to_blackhole_1000ms_latency	memory_usage	0/10
❌	file_to_blackhole_300ms_latency	memory_usage	0/10
❌	file_to_blackhole_500ms_latency	memory_usage	0/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	idle	memory_usage	10/10
✅	idle_all_features	memory_usage	10/10

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".