test(openfeature): demonstrate span enrichment type mismatch attribution

[leoromanovsky]

Motivation

The span-enrichment implementation in the base PR currently trusts serial ID metadata before checking whether OpenFeature returned the caller default for a type mismatch. The existing evaluation logging path treats TypeMismatchCode as a runtime default even when a variant is present; this stacked PR captures the same expectation for span enrichment.

Changes

• Add a TestBuildEvaluation case where the evaluation details contain a variant and serial ID metadata, but also carry TypeMismatchCode and the returned default value.
• Assert that span enrichment records the runtime default instead of serial attribution.

Decisions

• Keep this PR test-only and stacked on the base PR so it demonstrates the current behavior without changing the implementation.
• Leave the test failing until the parent branch handles TypeMismatchCode before the serial-ID branch.

Validation

• go test ./openfeature -run '^TestBuildEvaluation$' -count=1 fails on the new regression case: current code records subject/serial attribution and no default value.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 63.57%. Comparing base (2e59473) to head (d8e5624).

Additional details and impacted files

see 25 files with indirect coverage changes

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⚠️ Warnings

🚦 11 Pipeline jobs failed

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ℹ️ Info

No other issues found (see more)

🧪 All tests passed
❄️ No new flaky tests detected

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[chatgpt-codex-connector]

💡 Codex Review

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Reviewed commit: d8e5624783

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[chatgpt-codex-connector]

Fix the failing span-enrichment test

With this new case, go test ./openfeature -run '^TestBuildEvaluation$/type_mismatch' -count=1 fails: the current buildEvaluation path still treats the non-empty variant plus serial metadata as an assignment and returns Subject/SerialID with no DefaultValue, so the test-only commit breaks the package test suite until the implementation change lands in the same change set.

Useful? React with 👍 / 👎.

[pr-commenter]

Benchmarks

Benchmark execution time: 2026-06-25 04:13:10

Comparing candidate commit d8e5624 in PR branch leo/span-enrichment-type-mismatch-regression with baseline commit 2e59473 in branch oleksii/span-enrichment.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 298 metrics, 1 unstable metrics, 1 flaky benchmarks without significant changes.

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 ----------------------------------'

Known flaky benchmarks

These benchmarks are marked as flaky and will not trigger a failure. Modify FLAKY_BENCHMARKS_REGEX to control which benchmarks are marked as flaky.

Known flaky benchmarks without significant changes:

• scenario:BenchmarkOTLPTraceWriterFlush

[sameerank]

Good catch!