Test Result Aggregation & Dashboards

"""Aggregate JUnit XML results from multiple test runs."""
import xml.etree.ElementTree as ET
from pathlib import Path
from dataclasses import dataclass
from typing import List

@dataclass
class TestRunSummary:
    suite_name: str
    timestamp:  str
    total:      int
    passed:     int
    failed:     int
    errors:     int
    duration_s: float

    @property
    def pass_rate(self) -> float:
        return self.passed / max(self.total, 1) * 100

def aggregate_xml_reports(report_dir: str) -> List[TestRunSummary]:
    summaries = []
    for xml_file in Path(report_dir).glob("*.xml"):
        tree = ET.parse(xml_file)
        suite = tree.getroot()
        if suite.tag != "testsuite":
            suite = suite.find("testsuite") or suite
        total   = int(suite.get("tests",    0))
        failed  = int(suite.get("failures", 0))
        errors  = int(suite.get("errors",   0))
        passed  = total - failed - errors
        summaries.append(TestRunSummary(
            suite_name=suite.get("name", xml_file.stem),
            timestamp =suite.get("timestamp", ""),
            total=total, passed=passed,
            failed=failed, errors=errors,
            duration_s=float(suite.get("time", 0))
        ))
    return summaries

"""Detect flaky tests by analysing historical results."""
from collections import defaultdict
from typing import List, Dict

def detect_flaky_tests(
        run_history: List[Dict[str, str]],
        min_runs: int = 5,
        flaky_threshold_pct: float = 20.0) -> list:
    """A test is flaky if it fails in 5-95% of runs."""
    test_results = defaultdict(list)
    for run in run_history:
        for tc_id, verdict in run.items():
            test_results[tc_id].append(verdict)
    flaky = []
    for tc_id, verdicts in test_results.items():
        if len(verdicts) < min_runs:
            continue
        fail_rate = verdicts.count("FAIL") / len(verdicts) * 100
        if flaky_threshold_pct <= fail_rate <= (100 - flaky_threshold_pct):
            flaky.append({
                "tc_id": tc_id,
                "fail_rate_pct": round(fail_rate, 1),
                "runs": len(verdicts)
            })
    return sorted(flaky, key=lambda x: -x["fail_rate_pct"])