Cover top-level entry files in component label rules

[p-datadog]

What does this PR do?

Updates 5 rules in .github/labeler.yml so they match the component's top-level entry file (lib/datadog/<thing>.rb) in addition to the directory contents (lib/datadog/<thing>/**).

Rule	Entry file added
core	lib/datadog/core.rb
profiling	lib/datadog/profiling.rb
appsec	lib/datadog/appsec.rb
tracing	lib/datadog/tracing.rb
otel	lib/datadog/opentelemetry.rb

Motivation:

actions/labeler glob lib/datadog/<thing>/** matches files inside the directory but not the sibling lib/datadog/<thing>.rb entry-point file. A PR that touches only the entry file is not currently auto-labeled.

ci-app is unaffected — lib/datadog/ci.rb does not exist.

debugger (formerly debugging) is fixed in #5707.

Change log entry

None.

How to test the change?

The labeler workflow runs on pull_request events. Editing only lib/datadog/profiling.rb (or any of the five entry files above) on a future PR should now apply the corresponding component label.

[marcotc]

Nice catch!

[datadog-datadog-prod-us1-2]

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 97.15% (+0.01%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 7a10ea6 | Docs | Datadog PR Page | Give us feedback!}

[ivoanjo]

👍 LGTM, nice catch, spotted a few more

[Strech]

I know that we also have an AI Guard label now, does it make sense to add it too?

[pr-commenter]

Benchmarks

Benchmark execution time: 2026-05-11 12:27:22

Comparing candidate commit 39fb35a in PR branch labeler-cover-entry-files with baseline commit c05d989 in branch master.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 45 metrics, 1 unstable metrics.

Explanation

This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:

• 🟩 = significantly better candidate vs. baseline
• 🟥 = significantly worse candidate vs. baseline

We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.

If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.

Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.

More details about the CI and significant changes

You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.

CIs of the difference of means are often centered around 0%, because often changes are not that big:

---------------------------------(------|---^--------)-------------------------------->
                              -0.6%    0%  0.3%     +1.2%
                                 |          |        |
         lower bound of the CI --'          |        |
sample mean (center of the CI) -------------'        |
         upper bound of the CI ----------------------'

As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).

For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:

----------------------------------------|---------|---(---------^---------)---------->
                                       0%        1%  1.3%      2.2%      3.1%
                                                  |   |         |         |
       significant impact threshold --------------'   |         |         |
                      lower bound of CI --------------'         |         |
       sample mean (center of the CI) --------------------------'         |
                      upper bound of CI ----------------------------------'

[p-datadog]

Good call — added an ai-guard rule covering both lib/datadog/ai_guard.rb and lib/datadog/ai_guard/** in the latest commit.