Test reporting for every framework

Qualflare turns your CI test results into hosted, historical reporting with AI analysis — whatever framework you run. Its CLI auto-detects 23+ frameworks, then clusters failures by root cause, scores flaky tests, and rates each launch’s risk. Below are step-by-step reporting guides for the most popular ones.

Framework reporting guides

• Playwright test reporting

  Send Playwright JSON results — AI failure clustering and flaky scoring from Playwright’s native retry data.

• pytest test reporting

  Upload pytest --junitxml output — AI clustering, history-based flaky detection, and pytest-xdist aggregation.

• CucumberJS test reporting

  Add the native @qualflare/cucumberjs formatter — Gherkin step traces, Scenario Outline rows as cases, AI clustering and history-based flaky scoring.

• Mocha test reporting

  Add the native @qualflare/mocha reporter — Mocha’s built-in retries as per-attempt history, AI clustering, and --parallel runs merged into one launch.

• Vitest test reporting

  Add the native @qualflare/vitest reporter — retry history, steps and labels Vitest can’t record, plus AI clustering and flaky scoring across runs.

• Selenium test reporting

  Selenium has no report format of its own — upload your runner’s output and get per-browser flake attribution on top.

• PHPUnit test reporting

  Failures, errors and risky tests kept apart rather than flattened into one red count, plus data-provider rows as cases.

• TestCafe test reporting

  Quarantine-mode unstable tests recorded as genuinely flaky, with screenshots and video kept alongside the failure.

• Karate test reporting

  API scenarios as cases, Scenario Outline rows tracked separately, and tags carried through to the report.

• TestNG test reporting

  Upload Surefire’s JUnit-format XML — @DataProvider rows as cases, and flaky scoring that sees through retryAnalyzer.

• RSpec test reporting

  Use RSpec’s built-in JSON formatter — no extra gem, order-dependent flakiness surfaced, pending kept apart from skipped.

• Newman test reporting

  Send Postman collection runs — requests as cases with stable IDs across renames, plus iteration pass rates.

• k6 test reporting

  Track load tests across runs — checks as pass rates, thresholds as cases, and the failed checks that never fail your build.

• Go test reporting

  Run go test through the native qualflare-go reporter — every t.Run subtest as its own case, AI clustering, and history-based flaky detection.

• Cypress test reporting

  Send Cypress JUnit or mochawesome results — AI clustering and flaky scoring from Cypress’s built-in retries.

• Jest test reporting

  Send Jest JSON results — AI failure clustering and history-based flaky detection across every CI run and monorepo package.

• JUnit (Java) test reporting

  Upload Java JUnit XML from Maven Surefire or Gradle — AI clustering, flaky detection from the XML, and multi-module aggregation.

• Espresso test reporting

  Upload Android instrumented-test JUnit-XML from AndroidJUnitRunner — AI clustering and flaky detection, no emulator execution.

• XCTest test reporting

  Upload the .xcresult bundle directly on macOS — no conversion step — with AI clustering and flaky detection for iOS CI.

• Maestro test reporting

  Send Maestro’s native JUnit-XML output — unify Android + iOS flow results alongside Espresso and XCTest in one dashboard.

• Detox test reporting

  React Native E2E — Detox runs on Jest, so the Jest reporter covers it, and Detox’s screenshots, videos and device logs attach to the right tests.

Also works with Mocha, Selenium, Cucumber, TestNG, RSpec, k6, Newman and more — point the Qualflare CLI at your results file and it auto-detects the format.

How it works

Every guide follows the same two steps: have your test runner write a machine-readable results file (JSON or JUnit XML), then upload it with one CLI command — qf <project> collect <file>. The CLI attaches your Git branch and commit and turns each CI run into a tracked launch with AI failure clustering, flaky-test scoring, per-launch risk, and trends over time — the analysis a local HTML report can’t do.

Weighing platforms? See how Qualflare compares to other test management tools, read how to evaluate test observability platforms, or browse the best AI test management tools roundup. Setting up a cycle? Start with test planning. Counting the cost of flaky failures? Try the flaky test cost calculator. The analysis layer above per-run reporting is test observability.

Frequently asked questions