{
  "test_focus": "all",
  "data_status": "valid",
  "invalid_reason": "Unknown Invalid Data",
  "test_parameters": "focus=all, status=valid",
  "test_data": "country=CANADA, product=IPHONE 13 PRO, expected_message=YOUR ORDER HAS BEEN SUCCESSFULLY COMPLETED., last_name=yoon, password=Test123!, rowNum=1, first_name=garam, email=garam@gmail.com",
  "expected": "YOUR ORDER HAS BEEN SUCCESSFULLY COMPLETED.",
  "actual": "THANKYOU FOR THE ORDER.",
  "browser": "chrome",
  "os": "linux",
  "screenshot_url": "",
  "assertion_gap": "Expected 'YOUR ORDER HAS BEEN SUCCESSFULLY COMPLETED.' but got 'THANKYOU FOR THE ORDER.'",
  "is_ui_issue": false,
  "failure_type": "Logic Assertion",
  "ai_bucket": "flaky",
  "summary": "Inconsistent success message text",
  "root_cause": "```json\n{\n  \"FAILURE_CLASSIFICATION\": \"FLAKY\",\n  \"SUMMARY\": \"Inconsistent success message text\",\n  \"FLAKY_DESCRIPTION\": \"This appears to be a flaky test due to inconsistent success message content from the application. The test expects 'YOUR ORDER HAS BEEN SUCCESSFULLY COMPLETED.' but received 'THAN",
  "confidence": 75,
  "bug_description": "",
  "flaky_description": "This appears to be a flaky test due to inconsistent success message content from the application. The test expects 'YOUR ORDER HAS BEEN SUCCESSFULLY COMPLETED.' but received 'THANKYOU FOR THE ORDER.' - both messages indicate successful order completion but with different wording. This suggests either: (1) The application may be A/B testing different success messages, (2) There could be a race condition where different success message templates are loaded, (3) The backend might be returning different messages based on some non-deterministic factor, or (4) There could be multiple valid success message variations that the test doesn't account for. The test assertion is too rigid and doesn't accommodate valid message variations.",
  "impact": "",
  "root_cause_candidates": [],
  "log_line_references": [],
  "root_cause_analysis_jira": "_(No hypothesis scored above 90% — see full AI report and analysis JSON.)_",
  "recommendations": [
    "See AI analysis for details"
  ],
  "evidence": "```json\n{\n  \"FAILURE_CLASSIFICATION\": \"FLAKY\",\n  \"SUMMARY\": \"Inconsistent success message text\",\n  \"FLAKY_DESCRIPTION\": \"This appears to be a flaky test due to inconsistent success message content from the application. The test expects 'YOUR ORDER HAS BEEN SUCCESSFULLY COMPLETED.' but received 'THAN",
  "claude_ok": true,
  "error_detail": null,
  "run_id": "run-1776031531100",
  "test_id": "testCases.CheckOut#verifyCheckOutSuccess[VALID-ALL]",
  "failure_index": 0,
  "failures_in_bundle": 2,
  "artifact_file_stem": "flaky-run-1776031531100-01",
  "canonical_ai_id": "flaky-run-1776031531100-01",
  "triage_classification_summary": {
    "BUG": 1,
    "FLAKY": 0,
    "NEEDS_REVIEW": 1
  },
  "triage_latest_run_id": "run-1776031531100",
  "ai_analysis_excerpt": "```json\n{\n  \"FAILURE_CLASSIFICATION\": \"FLAKY\",\n  \"SUMMARY\": \"Inconsistent success message text\",\n  \"FLAKY_DESCRIPTION\": \"This appears to be a flaky test due to inconsistent success message content from the application. The test expects 'YOUR ORDER HAS BEEN SUCCESSFULLY COMPLETED.' but received 'THANKYOU FOR THE ORDER.' - both messages indicate successful order completion but with different wording. This suggests either: (1) The application may be A/B testing different success messages, (2) There could be a race condition where different success message templates are loaded, (3) The backend might be returning different messages based on some non-deterministic factor, or (4) There could be multiple valid success message variations that the test doesn't account for. The test assertion is too rigid and doesn't accommodate valid message variations.\",\n  \"IMPACT_ON_SYSTEM_QUALITY\": \"This flakiness creates CI noise by failing tests that actually represent successful checkout flows, just with different success message text. It leads to false negatives where valid application behavior is marked as failure. Teams waste significant time investigating these failures only to discover the functionality works correctly but displays an alternative success message. This erodes confidence in the test suite and may cause developers to ignore legitimate failures.\",\n  \"LOG_LINE_REFERENCES\": []\n}\n```",
  "artifacts": {
    "ai_bucket": "flaky",
    "canonical_ai_id": "flaky-run-1776031531100-01",
    "html_report": "reports/AI/flaky/report/flaky-run-1776031531100-01_ai_report.html",
    "analysis_json": "reports/AI/flaky/analysis/flaky-run-1776031531100-01_ai_rca.json",
    "summary_json": "reports/AI/flaky/summary/flaky-run-1776031531100-01_ai_summary.json"
  }
}