[ASCII-2321] add a new CODEOWNERS linter to check its accuracy

[misteriaud]

What does this PR do?

This PR introduce a new linter to check correctness of .github/CODEOWNERS.
It checks for each rule that at least one file or a directory match the given pattern in the repo.

Motivation

It happens that the CODEOWNERS file is not updated when files/directorys are moved/deleted due to refactoring or other reason. This leads to a inconsistant CODEOWNERS file.

Describe how to test/QA your changes

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 47041036 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_deb-arm64-py3
• tests_deb-x64-py3
• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-x64
• tests_rpm-arm64-py3
• tests_rpm-x64-py3
• tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 68c1db8e-5f52-4ea9-8084-fe37b2efd0a8 Metrics dashboard Target profiles

Baseline: 6cd87f7
Comparison: 66e7323

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
➖	quality_gate_idle_all_features	memory utilization	+1.60	[+1.48, +1.73]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	+1.28	[+1.20, +1.36]	1	Logs
➖	idle_all_features	memory utilization	+0.77	[+0.62, +0.92]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	+0.77	[-2.03, +3.58]	1	Logs
➖	file_tree	memory utilization	+0.69	[+0.56, +0.81]	1	Logs
➖	idle	memory utilization	+0.65	[+0.60, +0.70]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	+0.38	[+0.32, +0.43]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency	egress throughput	+0.02	[-0.30, +0.35]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.11, +0.09]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.02	[-0.21, +0.16]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.03	[-0.25, +0.20]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.16	[-0.65, +0.32]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.20	[-0.44, +0.05]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.72	[-1.53, +0.08]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.99	[-3.45, +1.47]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.17	[-1.88, -0.46]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	idle	memory_usage	10/10
✅	idle_all_features	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10
✅	quality_gate_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".

[misteriaud]

/merge

[dd-devflow]

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[misteriaud]

/remove

[dd-devflow]

🚂 Devflow: /remove

[dd-devflow]

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