File size: 4,676 Bytes
38be44d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | /**
* @license
* Copyright 2026 Google LLC
* SPDX-License-Identifier: Apache-2.0
*/
/**
* @fileoverview Compares PR evaluation results against historical nightly baselines.
*
* This script generates a Markdown report for use in PR comments. It aligns with
* the 6-day lookback logic to show accurate historical pass rates and filters out
* pre-existing or noisy failures to ensure only actionable regressions are reported.
*/
import fs from 'node:fs';
import path from 'node:path';
import { fetchNightlyHistory } from './eval_utils.js';
/**
* Main execution logic.
*/
function main() {
const prReportPath = 'evals/logs/pr_final_report.json';
const targetModel = process.argv[2];
if (!targetModel) {
console.error('❌ Error: No target model specified.');
process.exit(1);
}
if (!fs.existsSync(prReportPath)) {
console.error('No PR report found.');
return;
}
const prReport = JSON.parse(fs.readFileSync(prReportPath, 'utf-8'));
const history = fetchNightlyHistory(6); // Use same 6-day lookback
const latestNightly = aggregateHistoricalStats(history, targetModel);
const regressions = [];
const passes = [];
for (const [testName, pr] of Object.entries(prReport.results)) {
const prRate = pr.passed / pr.total;
if (pr.status === 'regression' || (prRate <= 0.34 && !pr.status)) {
// Use relative path from workspace root
const relativeFile = pr.file
? path.relative(process.cwd(), pr.file)
: 'evals/';
regressions.push({
name: testName,
file: relativeFile,
nightly: latestNightly[testName]
? (latestNightly[testName].passRate * 100).toFixed(0) + '%'
: 'N/A',
pr: (prRate * 100).toFixed(0) + '%',
});
} else {
passes.push(testName);
}
}
if (regressions.length > 0) {
let markdown = '### 🚨 Action Required: Eval Regressions Detected\n\n';
markdown += `**Model:** \`${targetModel}\`\n\n`;
markdown +=
'The following trustworthy evaluations passed on **`main`** and in **recent Nightly runs**, but failed in this PR. These regressions must be addressed before merging.\n\n';
markdown += '| Test Name | Nightly | PR Result | Status |\n';
markdown += '| :--- | :---: | :---: | :--- |\n';
for (const r of regressions) {
markdown += `| ${r.name} | ${r.nightly} | ${r.pr} | ❌ **Regression** |\n`;
}
markdown += `\n*The check passed or was cleared for ${passes.length} other trustworthy evaluations.*\n\n`;
markdown += '<details>\n';
markdown +=
'<summary><b>🛠️ Troubleshooting & Fix Instructions</b></summary>\n\n';
for (let i = 0; i < regressions.length; i++) {
const r = regressions[i];
if (regressions.length > 1) {
markdown += `### Failure ${i + 1}: ${r.name}\n\n`;
}
markdown += '#### 1. Ask Gemini CLI to fix it (Recommended)\n';
markdown += 'Copy and paste this prompt to the agent:\n';
markdown += '```text\n';
markdown += `The eval "${r.name}" in ${r.file} is failing. Investigate and fix it using the behavioral-evals skill.\n`;
markdown += '```\n\n';
markdown += '#### 2. Reproduce Locally\n';
markdown += 'Run the following command to see the failure trajectory:\n';
markdown += '```bash\n';
const pattern = r.name.replace(/'/g, '.');
markdown += `GEMINI_MODEL=${targetModel} npm run test:all_evals -- ${r.file} --testNamePattern="${pattern}"\n`;
markdown += '```\n\n';
if (i < regressions.length - 1) {
markdown += '---\n\n';
}
}
markdown += '#### 3. Manual Fix\n';
markdown +=
'See the [Fixing Guide](https://github.com/google-gemini/gemini-cli/blob/main/evals/README.md#fixing-evaluations) for detailed troubleshooting steps.\n';
markdown += '</details>\n';
process.stdout.write(markdown);
} else if (passes.length > 0) {
// Success State
process.stdout.write(
`✅ **${passes.length}** tests passed successfully on **${targetModel}**.\n`,
);
}
}
/**
* Aggregates stats from history for a specific model.
*/
function aggregateHistoricalStats(history, model) {
const stats = {};
for (const item of history) {
const modelStats = item.stats[model];
if (!modelStats) continue;
for (const [testName, stat] of Object.entries(modelStats)) {
if (!stats[testName]) stats[testName] = { passed: 0, total: 0 };
stats[testName].passed += stat.passed;
stats[testName].total += stat.total;
}
}
for (const name in stats) {
stats[name].passRate = stats[name].passed / stats[name].total;
}
return stats;
}
main();
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