How to Use AI and Analytics in TestOps

Nazneen AhmadApril 18, 2025About 6 min

How to Use AI and Analytics in TestOps 관련

How to Scale TestOps for Global Software Development Teams

Imagine that your software team is spread across the globe—developers in the US, testers in Asia, and managers in Europe. Exciting, right? But managing this setup is no walk in the park. Coordinating testing across time zones, tools, and workflows ca...

How to Scale TestOps for Global Software Development Teams

When you bring AI and analytics into TestOps, it helps simplify complex testing tasks. It reduces manual work, improves accuracy, and supports better decision-making. Since DevOps teams often work across regions, this becomes even more important.

AI helps reduce repetitive testing tasks, while analytics turns test data into clear insights. Together, they create smarter and faster testing pipelines. And because these pipelines are shared globally, consistency is key.

What Tools Can You Use?

There are different tools that support AI and analytics in TestOps. Some focus on automation with intelligence, while others give you clear visibility into your test data.

AI-powered testing tools like Mabl, Testim, and Functionize use machine learning. These tools help create, run, and even repair test cases when the app changes. Since applications change frequently, these tools help keep your tests up to date.

They also save time on maintenance, since the tests adjust themselves when needed. And because the tools learn from patterns, they help teams catch issues faster.

Analytics and observability platforms such as TestRail Analytics, Xray, Grafana, and Kibana focus on trends. They turn raw test results into visual dashboards and alerts.

These platforms connect with CI/CD tools, so you get real-time updates on test quality. This makes it easier for teams to stay on top of what matters, even when they are spread across locations.

Example – Running a Working Test on LambdaTest

LambdaTest lets you execute real browser tests in the cloud, making it easy to scale your testing across browsers and OS combinations. Here is a working example using Python and Selenium, which opens a page, checks the title, and closes the browser:

from selenium import webdriver
from selenium.webdriver.common.by import By

# Define LambdaTest capabilities
capabilities = {
  "browserName": "Chrome",
  "browserVersion": "latest",
  "LT:Options": {
    "platformName": "Windows 11",
    "build": "TestOps Working Demo",
    "name": "Title Verification Test",
    "selenium_version": "4.8.0",
    "w3c": True
  }
}

# Replace with your LambdaTest username and access key
USERNAME = "your_username"
ACCESS_KEY = "your_access_key"

# Connect to LambdaTest cloud grid
driver = webdriver.Remote(
    command_executor=f"https://{USERNAME}:{ACCESS_KEY}@hub.lambdatest.com/wd/hub",
    desired_capabilities=capabilities
)

try:
    # Step 1: Navigate to the app under test
    driver.get("https://www.lambdatest.com/selenium-playground/")

    # Step 2: Interact with the page (click a link)
    driver.find_element(By.LINK_TEXT, "Simple Form Demo").click()

    # Step 3: Enter message and verify output
    message_box = driver.find_element(By.ID, "user-message")
    message_box.send_keys("TestOps in action!")
    driver.find_element(By.ID, "showInput").click()

    output = driver.find_element(By.ID, "message").text
    assert output == "TestOps in action!", "Message output did not match input."

    print("✅ Test Passed: Message displayed correctly.")
except Exception as e:
    print("❌ Test Failed:", e)
finally:
    driver.quit()

What this test does:

Launches a browser on LambdaTest cloud
Navigates to their Selenium Playground
Fills out a form and clicks a button
Verifies that the output matches the input
Logs the result and closes the session

Once the test finishes, you can view detailed logs, screenshots, and video recordings on the LambdaTest Automation Dashboard, which also includes AI-based debugging info.

What Types of Problems Can ML Help Solve — and How?

Machine learning can solve several pain points in testing. It looks at patterns in your data and helps identify things that manual checks might miss.

Flaky Tests

ML helps detect tests that pass and fail randomly across different builds. It finds patterns in those failures and flags the ones that are unstable. And by doing this early, it prevents teams from wasting time chasing false bugs.

Test Prioritization

ML studies your past test results and recent code changes. It then ranks your tests based on risk and importance. So, the most critical ones run first. This way, your pipeline moves faster without skipping key checks.

Failure Prediction

ML uses logs, crash reports, and previous outcomes to predict where failures may happen. If it finds something risky, it warns the team in advance. This gives them time to fix problems before they grow bigger.

Root Cause Clustering

When many tests fail at once, ML groups them by shared failure reasons. It helps you understand whether the issue is with one module or across several. That means your team can solve the actual root problem quicker.

Anomaly Detection

ML tracks things like test duration and system behavior. If something suddenly changes, like a test taking too long or using too much memory, it flags it. These alerts help teams spot performance dips early.

What Types of Analytics Tools Can You Use?

Analytics tools help you turn your test results into useful information. They highlight patterns, gaps, and areas that need your attention. And because these insights are visual, they are easier to act on.

These tools can show how your pass/fail rates have changed over time. They also help you check which parts of your app are not covered by tests. If some tests are being skipped or are too flaky, the tools will highlight that too.

They also measure how long tests take to run and where your pipeline slows down. This helps teams reduce bottlenecks and improve efficiency.

Some platforms include dashboards that link test quality to deployment status. This gives you a clear picture of whether your product is ready for release.

They also track failures by environment—like which browsers or regions face more issues. This helps teams debug faster and improve global reliability.

All of these insights help QA and DevOps teams improve their strategies. They let you remove unnecessary tests, fix flaky ones, and focus where testing matters the most.

Example – Using Grafana and Kibana for Test Analytics

Analytics tools help your teams understand test trends, flakiness, coverage gaps, and slowdowns in the CI/CD pipeline. Here is how you can set them up to actually work with your test data.

Example 1: Visualizing Test Results in Grafana using InfluxDB

Grafana is commonly paired with InfluxDB to display metrics such as pass/fail rates, test durations, and failure frequencies. Here’s how you can push your test results into InfluxDB and visualize them in Grafana.

Step-by-step setup:

Push test results to InfluxDB after each test run. This can be done from Jenkins, GitHub Actions, or any test automation framework that generates test results.
Query and visualize data in Grafana using InfluxDB as the data source.

Python script to send test metrics to InfluxDB:

from influxdb import InfluxDBClient
import time

# Create an InfluxDB client to send data
client = InfluxDBClient(host='localhost', port=8086)
client.switch_database('test_metrics')  # Switch to your specific database

# Example test result data
json_body = [
    {
        "measurement": "test_results",
        "tags": {
            "test_suite": "login_tests",  # Name of the test suite
            "environment": "staging"  # Environment like 'production', 'staging', etc.
        },
        "time": time.strftime('%Y-%m-%dT%H:%M:%SZ'),  # Timestamp for the test execution
        "fields": {
            "pass": 10,        # Number of tests that passed
            "fail": 2,         # Number of tests that failed
            "skipped": 1,      # Number of tests that were skipped
            "duration": 12.5   # Duration of the test run in seconds
        }
    }
]

# Write the data points to InfluxDB
client.write_points(json_body)

Grafana setup:

Data Source: In Grafana, connect to your InfluxDB instance.
Dashboard: Create a dashboard that queries the test_results measurement and display:
- Line charts for pass/fail trends over time.
- Pie charts for distribution of test results.
- Table showing tests and their durations.

This approach helps you track key metrics and trends for each test suite and environment.

Example 2: Debugging Failures by Environment with Kibana and Elasticsearch

If your testing framework logs results into Elasticsearch, you can use Kibana to analyze and visualize those logs. For example, you can track which browsers or regions are facing more issues and display the results in Kibana.

Elasticsearch Data Model:

First, let’s assume that test results are logged in Elasticsearch with the following format:

{
  "timestamp": "2025-04-16T14:00:00Z",
  "test_name": "checkout_flow_mobile",  # Name of the test
  "status": "fail",  # Pass or fail status
  "browser": "Safari",  # Browser used for the test
  "region": "EU-West",  # Region where the test was run
  "error": "Element not visible",  # Error message in case of failure
  "duration": 9.8,  # Duration of the test in seconds
  "env": "QA"  # Environment where the test ran
}

Kibana setup:

Data Ingestion: Your CI pipeline or test scripts push results to Elasticsearch after each run.
Create Visualizations: In Kibana, create visualizations like:
- Pie Chart: Show failure rates by browser type (e.g., Chrome, Firefox, Safari).
- Line Chart: Track test durations over time for a specific test suite.
- Table: Display flaky tests that fail repeatedly by region or environment.

Example of a Kibana query that you might use to filter failures by browser:

{
  "query": {
    "bool": {
      "must": [
        { "match": { "status": "fail" }},
        { "match": { "browser": "Safari" }}
      ]
    }
  }
}

This will show all test failures in Safari, helping you identify browser-specific issues.

Why This Matters

Using Grafana and Kibana with your test results helps your team gain valuable insights:

Identify flaky tests and prioritize them for maintenance.
Track performance trends, including test duration and failure rates.
Debug faster by identifying failure patterns tied to specific browsers, environments, or regions.

With these analytics in place, teams can make data-driven decisions to improve test coverage, reduce bottlenecks, and ensure better product quality.