Daily Job Summaries: Add tests link

[CelianR]

What does this PR do?

This PR updates the daily job summary by providing an additional link to each job for their respective failing tests.

Discussion.

Motivation

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

Report before this change:

• Example failing job and its failing tests

[agent-platform-auto-pr[bot]]

Gitlab CI Configuration Changes

Modified Jobs

notify_failure_summary_daily

  notify_failure_summary_daily:
    dependencies: []
    image: 486234852809.dkr.ecr.us-east-1.amazonaws.com/ci/datadog-agent-buildimages/deb_x64$DATADOG_AGENT_BUILDIMAGES_SUFFIX:$DATADOG_AGENT_BUILDIMAGES
+   needs: []
    resource_group: notification
-   rules:
-   - if: $CI_COMMIT_BRANCH != "main" || $CI_PIPELINE_SOURCE != "schedule"
-     when: never
-   - if: $BUCKET_BRANCH != "nightly" && $BUCKET_BRANCH != "oldnightly" && $BUCKET_BRANCH
-       != "dev"
-     when: never
-   - if: $DEPLOY_AGENT == "true" || $DDR_WORKFLOW_ID != null
-     when: always
    script:
    - SLACK_API_TOKEN=$($CI_PROJECT_DIR/tools/ci/fetch_secret.sh $SLACK_AGENT token)
      || exit $?; export SLACK_API_TOKEN
    - GITLAB_TOKEN=$($CI_PROJECT_DIR/tools/ci/fetch_secret.sh $GITLAB_TOKEN read_api)
      || exit $?; export GITLAB_TOKEN
    - DD_API_KEY=$($CI_PROJECT_DIR/tools/ci/fetch_secret.sh $AGENT_API_KEY_ORG2 token)
      || exit $?; export DD_API_KEY
    - python3 -m pip install -r requirements.txt -r tasks/libs/requirements-notifications.txt
-   - weekday="$(date --utc '+%A')"
-   - "if [ \"$weekday\" = \"Sunday\" ] || [ \"$weekday\" = \"Monday\" ]; then\n  echo\
-     \ \"Skipping daily summary on $weekday\"\n  exit\nfi\n"
-   - inv -e notify.failure-summary-send-notifications --daily-summary
+   - inv -e notify.failure-summary-send-notifications --daily-summary --dry-run
?                                                                     ++++++++++
-   - "if [ \"$weekday\" = \"Friday\" ]; then\n  echo 'Sending weekly summary'\n  inv\
-     \ -e notify.failure-summary-send-notifications --weekly-summary\nfi\n"
    stage: notify
    tags:
    - arch:amd64
    timeout: 15 minutes

Changes Summary

Removed	Modified	Added	Renamed
0	1	0	0

:information_source: Diff available in the job log.

[agent-platform-auto-pr[bot]]

[Fast Unit Tests Report]

On pipeline 47212603 (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[bot]]

Regression Detector

Regression Detector Results

Run ID: bc260189-e2c3-4e76-951e-7d15404c737f Metrics dashboard Target profiles

Baseline: e79e0622abc05885bd2b0f9c2b96e245781e3c01 Comparison: a046332d87482d34b66e8006b45a860264fa60c1

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
➖	file_tree	memory utilization	+3.15	[+3.01, +3.30]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+1.47	[-1.11, +4.04]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+1.43	[+0.62, +2.24]	1	Logs
➖	idle_all_features	memory utilization	+0.70	[+0.59, +0.81]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	+0.62	[+0.57, +0.68]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	+0.29	[+0.24, +0.34]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.03	[-0.20, +0.25]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.32, +0.34]	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_500ms_latency	egress throughput	-0.01	[-0.25, +0.24]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.01	[-0.20, +0.18]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.09	[-0.58, +0.40]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.15	[-0.87, +0.58]	1	Logs
➖	idle	memory utilization	-0.36	[-0.41, -0.31]	1	Logs bounds checks dashboard
➖	quality_gate_idle_all_features	memory utilization	-0.75	[-0.85, -0.64]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	-0.93	[-3.68, +1.82]	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".

[CelianR]

Not needed anymore