AI Root Cause Analysis (AI RCA) - Test Intelligence

AI Root Cause Analysis (AI RCA) in LambdaTest Analytics automatically analyzes failed tests to identify probable root causes and provides actionable insights to improve test stability and reduce debugging time. This AI-powered feature accelerates your debugging process by eliminating manual investigation and providing instant, comprehensive failure analysis.

What is AI Root Cause Analysis?

AI RCA is an intelligent feature that uses advanced machine learning algorithms to automatically analyze test failures and identify their root causes. Instead of manually sifting through logs and error messages, AI RCA provides:

• Automated failure analysis that runs immediately when tests fail
• Intelligent root cause identification distinguishing between primary causes and cascading symptoms
• Actionable steps to fix with specific fixes and recommendations
• Historical trend analysis to identify recurring issues and patterns
• Comprehensive error timelines showing the sequence of events leading to failures

Prerequisites for AI RCA

1. Active LambdaTest Account: You should have an active LambdaTest account with appropriate permissions.
2. Subscription Plan: This feature is available for users with HyperExecute or App/Web Automation subscription plans.
3. Available Credits: Tests will only be processed through RCA when sufficient credits are available. For more information on credit management, see our Credits Management documentation.
4. Test Failures: AI RCA requires at least one test failure to generate analysis. The system learns from your test execution patterns.
5. Access to Insights: You should have access to the LambdaTest Insights platform under Insights tab.

Configuration & Setup

Step 1: Access Organization Settings

1. Navigate to Organization Settings in your LambdaTest dashboard
2. In the left sidebar, expand the Insights section under Org Product Preferences
3. Select Automatic AI RCA from the available options

Step 2: Enable AI RCA

1. Toggle the Feature: Use the blue toggle switch to enable "Automatic AI RCA"
2. Configure Analysis Scope: Choose which types of test failures to analyze:
   • All failures: Analyze every failed test, regardless of previous status
   • New failures: Analyze only tests that have failed recently after having passed at least 10 consecutive times previously.
   • Always failing: Analyze only tests that have failed in all of their previous 5 runs to identify persistent issues.

Step 3: Set Special Instructions (Optional)

Provide context or specific guidance for the AI to consider during analysis:

1. Click on the Special Instructions section
2. Enter any special instructions or context that should be considered during AI root cause analysis
3. Use the "Show examples" link for guidance on effective instruction writing

Example Instructions:

tip

Our CRM application has specific failure patterns to watch for:

PRIORITY CATEGORIES

1. Database Connection Issues - Our PostgreSQL connection pool is limited to 20 connections. Look for connection timeouts, pool exhaustion, or slow query performance.

2. Third-party API Failures - We integrate with Salesforce, HubSpot, and Mailchimp. These external APIs often have rate limits and intermittent failures that cause our tests to fail.

3. File Upload/Processing Issues - Contact import via CSV files often fails due to file size limits (10MB max) or malformed data. Check for upload timeouts and validation errors.

4. Authentication/Authorization - We use OAuth 2.0 with multiple providers. Token expiration and permission changes frequently cause test failures.

5. UI Element Timing Issues - Our CRM uses dynamic loading for contact lists and reports. Elements may not be ready when tests try to interact with them.

SPECIFIC CONTEXT

• Our test environment has limited resources compared to production
• We run tests during business hours when external APIs are under heavy load
• Focus on identifying whether failures are environment-specific or application bugs
• Prioritize failures that affect core CRM functionality (contact management, lead tracking, reporting)
• Consider our custom error handling - we log all errors to Sentry and show user-friendly messages

IGNORE THESE COMMON FALSE POSITIVES

• Browser console warnings that don't affect functionality
• Network requests to analytics services (Google Analytics, Hotjar)
• Minor UI layout shifts that don't break functionality

Possible Categories and Descriptions:

Category	Description
Database Issues	Connection timeouts, query performance, data integrity problems
API Integration	Third-party service failures, rate limiting, authentication issues
UI/UX Problems	Element not found, timing issues, responsive design failures
Performance Issues	Slow page loads, memory leaks, resource exhaustion
Environment Issues	Test data problems, configuration mismatches, infrastructure failures
Authentication/Authorization	Login failures, permission errors, session timeouts
File Processing	Upload failures, format validation, processing timeouts
Network Issues	Connectivity problems, DNS failures, proxy issues

Step 4: Configure Intelligent Targeting

Configure intelligent targeting rules to precisely control which tests, builds, tags, or jobs are included in AI-powered analysis:

1. Add Targeting Rules: Enter regex patterns in the input field
2. Click Include (+) or Exclude (-): Choose whether to include or exclude matching tests
3. Configure Multiple Criteria: Set targeting rules for:
   • Test Names: Target specific test suites or test patterns
   • Build Names: Include or exclude builds with specific names (e.g., hourly, nightly)
   • Test Tags: Include or exclude tests with specific tags (e.g., playwright_test, atxHyperexecute_test)
   • Build Tags: Include or exclude builds with specific tags (e.g., hourly, nightly)
   • Job Labels: Include tests with specific job labels or tags

Rule Logic and Application

The intelligent targeting system applies rules using the following logic:

Rule Evaluation Process:

1. Include Rules (AND Logic): All Include rules within the same category must match for a test to be considered
2. Exclude Rules (OR Logic): Any Exclude rule that matches will immediately exclude the test from analysis
3. Cross-Category Logic: Include rules across different categories (Test Names, Build Tags, etc.) must ALL match
4. Exclusion Precedence: Exclude rules take priority over Include rules - if any exclude rule matches, the test is excluded regardless of include matches

Best Practices for Rule Configuration:

• Start Broad: Begin with general include rules, then add specific exclusions
• Use Specific Patterns: Avoid overly broad regex patterns that might include unintended tests
• Test Your Rules: Verify rule behavior with sample test names and tags before applying
• Regular Review: Periodically review and update rules based on changing test patterns

Example Configuration for Production Test Analysis

tip

Test Name:

• Include: .*prod.* - Only analyze tests with name containing "prod"
• Exclude: .*non-critical.* - Skip tests with name containing "non-critical"

Build Tags:

• Include: ^hourly - Only analyze builds with tag starting with "hourly"

Test Tags:

• Include: playwright_test|atxHyperexecute_test - Focus on specific test frameworks
• Exclude: .*smoke.* - Skip smoke tests

Result: AI-powered analysis will run only on production tests (excluding non-critical ones) from hourly builds, focusing on Playwright or HyperExecute test tags, while excluding smoke tests. This configuration helps narrow down analysis to the most critical test scenarios.

Step 5: Save Configuration

1. Click Save Configuration to apply your settings
2. The settings will be applied to all users in your organization and cannot be modified by individual users or need admin level privileges.

RCA Output & Interpretation

Where to View RCA Results

AI RCA results are available in multiple locations across the LambdaTest platform:

1. TMS Dashboard: View RCA results directly in your test execution dashboard
2. HyperExecute Dashboard: Access detailed RCA analysis for HyperExecute jobs
3. Insights Dashboard: Comprehensive RCA analytics and trend analysis

Understanding RCA Output

The RCA output provides a unified analysis with:

• Primary Root Cause: The main issue identified by AI analysis
• Cascading Symptoms: Secondary issues that contribute to the problem but aren't the primary cause
• Severity Level: High, Medium, or Low severity classification
• Error Category: JavaScript Error, Network Error, Environment Issue, etc.

View Example RCA

Example RCA Summary:

Problem: Contact creation test fails after 30 seconds
Root Cause: Database connection pool is full (10/10 connections used)
Impact: New contact requests wait in queue until connections free up
Fix: Increase connection pool from 10 to 50 connections

Error Timeline

The Error Timeline provides a chronological sequence of events leading to the failure:

Example Timeline for CRM Contact Creation Test Failure:

1. Test starts - Navigate to contacts page (1s)
2. Fill form - Enter contact details (3s)
3. Click submit - Create contact button clicked (4s)
4. Connection issue - Database pool full, request queued (4s)
5. Wait timeout - No connections available (30s)
6. Test fails - "Contact creation timeout"

Steps to Fix

Each RCA includes specific, actionable steps to fix with clear implementation guidance:

Database Connection Issues:

• Quick Fix: Increase connection pool from 10 to 50
• Code: spring.datasource.hikari.maximum-pool-size=50
• Test Fix: Add connection health check before form submission

Form Submission Failures:

• Quick Fix: Add retry logic with 3 attempts
• Code: retry(createContact, { maxAttempts: 3, delay: 2000 })
• Test Fix: Mock database calls in test environment

Performance Issues:

• Quick Fix: Increase timeout to 45 seconds
• Code: waitForElement('#success-message', { timeout: 45000 })
• Test Fix: Use lightweight test data

RCA Category Trends Widget

The RCA Category Trends widget in Insights enables you to:

1. View Historical Trends: See how different RCA categories have evolved over time
2. Drill Down Analysis: Click on specific categories to analyze test failure patterns
3. Identify Recurring Issues: Spot patterns in failure types to prioritize fixes
4. Track Improvement: Monitor the effectiveness of your remediation efforts

Best Practices

1. Effective Configuration

• Start with "All failures" to get comprehensive coverage, then refine based on your needs
• Use specific special instructions to guide the AI toward your most critical issues
• Set up intelligent targeting to focus on relevant test suites and exclude noise

2. Interpreting Results

• Focus on primary root causes rather than cascading symptoms
• Prioritize high-severity issues for immediate attention
• Use the error timeline to understand the sequence of events
• Apply steps to fix systematically starting with the most critical fixes

3. Continuous Improvement

• Review RCA accuracy and provide feedback when possible
• Monitor trend analysis to identify recurring patterns
• Update special instructions based on new insights and requirements
• Share RCA results with your team to improve collective understanding

Troubleshooting Common Issues

RCA Not Generating

• Check prerequisites: Ensure you have the required subscription plan
• Verify credits availability: AI RCA processing requires credits to be available in your account. Check your Credits Management page to ensure sufficient credits
• Verify test failures: AI RCA requires actual test failures to analyze
• Review configuration: Confirm AI RCA is enabled in organization settings
• Check permissions: Ensure you have access to Analytics and Test Intelligence features

Inaccurate RCA Results

• Refine special instructions: Provide more specific context about your application
• Update intelligent targeting: Exclude irrelevant tests that might confuse the analysis
• Review error categorization: Ensure test failures are properly categorized
• Provide feedback: Use any available feedback mechanisms to improve accuracy

Missing RCA Data

• Check time range: Ensure you're looking at the correct time period
• Verify test execution: Confirm tests actually failed during the specified period
• Review dashboard filters: Check if any filters are excluding relevant data
• Contact support: Reach out if RCA data appears to be missing

Support

For any queries or issues related to AI Root Cause Analysis, please reach out to our 24/7 customer support. We're here to help you maximize the value of this powerful debugging tool!

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Related Documentation:

• Smart Tags - Test Intelligence
• Failure Categorization AI
• Test Insights Overview