feat(issues): Enable a new issue workflow for automatic issue triage

[chouetz]

What does this PR do?

Create a new Github workflow for datadog-agent issues, which assign automatically to a team.

• This workflow will first try to use a generated model to detect the impacted team, and fallback on an heuristic assignation otherwise.
• It relies on a docker image built locally using this Dockerfile and pushed in the Github Container Registry.
  • It's built locally because it requires a collection of all PR/issues from datadog-agent repo, which is tedious with rate limitation.
  • The generated model size (~250MB) is bigger than the max file size limit allowed by Github
• The workflow sends a direct slack notification to the concerned team, and defaults to #agent-ask-anything when no match is found.

Motivation

Improve our reactivity on issues opened in the repository, especially as we are a public repo. Besides, we have sometimes issues opened here faster than our support channel

Additional Notes

The model generation code is also shipped with this PR

Possible Drawbacks / Trade-offs

The model was generated using past data (issues and PR) and used mappings between old and new teams. We might need to re-generate it from time to time to have it accurate.

Describe how to test/QA your changes

Successful test here

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 50274088 (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

[pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 71d93ed2-9054-4ca9-b6ee-5f34d78fc230

Baseline: 407d3f8
Comparison: 22e2875
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	basic_py_check	% cpu utilization	+3.69	[-0.20, +7.58]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+2.08	[+1.34, +2.83]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+1.62	[+0.87, +2.37]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.31	[+0.25, +0.37]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.10	[-0.68, +0.87]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.02	[-0.76, +0.80]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.11, +0.12]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.01	[-0.70, +0.71]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.02	[-0.65, +0.61]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.08	[-0.86, +0.70]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.13	[-0.61, +0.34]	1	Logs
➖	file_tree	memory utilization	-0.34	[-0.48, -0.21]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.87	[-1.02, -0.72]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	-0.93	[-1.00, -0.86]	1	Logs bounds checks dashboard
➖	pycheck_lots_of_tags	% cpu utilization	-2.05	[-5.50, +1.40]	1	Logs
➖	quality_gate_logs	% cpu utilization	-2.44	[-5.34, +0.46]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	memory_usage	10/10

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

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

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".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.

[chouetz]

To update the label

[chouetz]

The definition of the container is here, and the model generation code is here

[chouetz]

/merge

[dd-devflow]

Devflow running: /merge

View all feedbacks in Devflow UI.

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2024-12-03 15:50:45 UTC ℹ️ MergeQueue: waiting for PR to be ready

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2024-12-03 16:17:33 UTC ℹ️ MergeQueue: merge request added to the queue

The median merge time in main is 23m.