Fix e2e tests

[mftoure]

What does this PR do?

This PR aims to fix the e2e tests by updating the download policy cli command to reflect the changes from the download policies endpoint

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[codecov]

Codecov Report

Attention: Patch coverage is 1.96078% with 50 lines in your changes are missing coverage. Please review.

Project coverage is 35.85%. Comparing base (d47c6d7) to head (2cb51c5).
Report is 19 commits behind head on main.

Files	Patch %	Lines
cmd/security-agent/subcommands/runtime/command.go	1.96%	50 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##             main   #25851       +/-   ##
===========================================
- Coverage   45.03%   35.85%    -9.18%     
===========================================
  Files        2314       15     -2299     
  Lines      266595     2468   -264127     
===========================================
- Hits       120062      885   -119177     
+ Misses     136975     1575   -135400     
+ Partials     9558        8     -9550     

Flag	Coverage Δ
amzn_aarch64	36.74% <1.96%> (?)
centos_x86_64	36.74% <1.96%> (-8.99%)	⬇️
ubuntu_aarch64	36.74% <1.96%> (-9.09%)	⬇️
ubuntu_x86_64	36.74% <1.96%> (-9.08%)	⬇️
windows_amd64	28.93% <ø> (-22.31%)	⬇️

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[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=34953817 --os-family=ubuntu

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 1d114438-9b6a-4ed7-9bc5-1ad95fb66768
Baseline: d47c6d7
Comparison: 2cb51c5

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
➖	pycheck_1000_100byte_tags	% cpu utilization	+0.65	[-4.03, +5.33]
➖	idle	memory utilization	+0.17	[+0.13, +0.22]
➖	otel_to_otel_logs	ingress throughput	+0.08	[-0.30, +0.45]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.20, +0.20]
➖	trace_agent_json	ingress throughput	-0.00	[-0.01, +0.01]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.04, +0.03]
➖	trace_agent_msgpack	ingress throughput	-0.01	[-0.01, +0.00]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.97	[-3.74, +1.80]
➖	file_tree	memory utilization	-1.36	[-1.45, -1.26]
➖	basic_py_check	% cpu utilization	-2.01	[-4.53, +0.51]
➖	tcp_syslog_to_blackhole	ingress throughput	-4.78	[-25.12, +15.55]

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".

[mftoure]

/merge

[dd-devflow]

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