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Automated Integration Test Goldens Update from CI#6133

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shixiao-coder merged 1 commit intomasterfrom
nl-golden-update-c55fb9ce-2341-426f-bd40-ab68e2566181
Mar 26, 2026
Merged

Automated Integration Test Goldens Update from CI#6133
shixiao-coder merged 1 commit intomasterfrom
nl-golden-update-c55fb9ce-2341-426f-bd40-ab68e2566181

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This pull request updates the golden files automatically via Cloud Build. Please review the changes carefully. Cloud Build Log

@datacommons-robot-author datacommons-robot-author requested review from a team and clincoln8 and removed request for a team March 26, 2026 18:20
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Summary of Changes

Hello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request synchronizes the integration test golden files with the latest outputs generated by the continuous integration system. The primary goal is to ensure that automated tests accurately reflect the current application behavior and prevent potential failures caused by stale reference data, thereby maintaining the reliability of the test suite.

Highlights

  • Automated Golden File Update: Integration test golden files have been automatically updated by the CI pipeline to reflect current expected outputs.
  • Chart Configuration Adjustment: An outdated 'quantity' classification object was removed from the 'chart_config.json' file for the 'stateswithhighestphds' test data.
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Code Review

This pull request removes a classification entry from the chart_config.json test data file, specifically a quantity check using a high-precision floating-point value. I have no feedback to provide as the review comment explains the rationale for the change rather than identifying a new issue in the updated code.

"idx": 0,
"qval": {
"cmp": "GE",
"val": 2.2250738585072014e-308
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medium

The floating-point value 2.2250738585072014e-308 in this test data file should be rounded to a consistent, lower precision. High-precision floating-point scores in test data can lead to fragile tests due to insignificant model output variations. Rounding helps make the tests more robust and reduces review noise.

References
  1. Round floating-point scores in test data to a consistent, lower precision. This makes tests more robust against insignificant model output variations and reduces review noise.

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LGTM

@shixiao-coder shixiao-coder merged commit e6ffff0 into master Mar 26, 2026
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@shixiao-coder shixiao-coder deleted the nl-golden-update-c55fb9ce-2341-426f-bd40-ab68e2566181 branch March 26, 2026 20:22
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