[SINT-1841] Dogfood Datadog SCA

[juliendoutre]

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What does this PR do?

Add a new Gitlab CI job listing this repository dependencies with trivy and uploading them to Datadog. This will make it possible to use the SCA product for this repository (see https://app.datadoghq.com/ci/code-analysis?limit=100&offset=0).

Motivation

@DataDog/software-integrity-and-trust partners with @DataDog/static-analysis to dogfood their SCA product and secure Datadog's supply chain.

Additional Notes

I created and saved an app key in the AWS build-stable account under path: ci.datadog-agent.datadog_app_key_org2 with appropriate permissions.

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Successful CI execution: https://gitlab.ddbuild.io/DataDog/datadog-agent/-/jobs/439355805

Navigate to https://app.datadoghq.com/ci/code-analysis?limit=100&offset=0 and check DataDog/datadog-agent is visible in the list. Click it, and inspect its dependencies.

[bits-bot]

All committers have signed the CLA.

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: f981ba79-4499-4abe-a121-8a1fdaaf3888 Baseline: 4bf26c276f788b974939b86c00cda694b907007e Comparison: 62e5c17e142618b3a39f61501a3b8be10cf75203 Total CPUs: 7

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

Experiments with missing or malformed data

• basic_py_check

Usually, this warning means that there is no usable optimization goal data for that experiment, which could be a result of misconfiguration.

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.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	-0.67	[-7.22, +5.88]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	otel_to_otel_logs	ingress throughput	+0.50	[-0.11, +1.11]
➖	process_agent_real_time_mode	memory utilization	+0.43	[+0.39, +0.48]
➖	tcp_syslog_to_blackhole	ingress throughput	+0.27	[+0.21, +0.33]
➖	process_agent_standard_check	memory utilization	+0.26	[+0.22, +0.31]
➖	file_tree	memory utilization	+0.26	[+0.19, +0.34]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_msgpack	ingress throughput	-0.03	[-0.04, -0.01]
➖	trace_agent_json	ingress throughput	-0.03	[-0.07, +0.01]
➖	process_agent_standard_check_with_stats	memory utilization	-0.11	[-0.15, -0.07]
➖	idle	memory utilization	-0.28	[-0.32, -0.23]
➖	file_to_blackhole	% cpu utilization	-0.67	[-7.22, +5.88]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.04	[-2.47, +0.38]

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".

[juliendoutre]

/merge

[dd-devflow[bot]]

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