AI Failure Analysis

Overview

AI Failure Analysis provides intelligent insights at both session and build levels to help teams quickly identify, prioritize, and resolve test failures and performance issues. Once Session-Level AI Analysis is completed, Build-Level Analysis can be enabled to aggregate insights across all analyzed sessions within a build.

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Important - To View Build-Level Analysis, the Session-level AI analysis must be completed first

Important Note: To View Build-Level Analysis, the Session-level AI analysis must be completed first

Prerequisites:

Session-level AI analysis must be completed first

Steps

Step 1: To Enable Session-Level AI Analysis

Navigate to the Sessions section below
Click on any session you want to analyze
View/enable AI analysis for that session

Step 2: To View Build-Level Insights

Navigate to related Build
Enable the toggle
Data will automatically aggregate across all analyzed sessions
Build-level insights appear in the sections above (Error Trends, Build Performance, Device Test Results)

1. Error Trends

What is Error Trends?

Error Trends identifies the most common error patterns across all test sessions in your build, helping you prioritize fixes based on frequency and impact.

How it Works:

Analyzes error logs from all sessions
Groups similar errors together
Ranks errors by occurrence frequency
Shows affected session count for each error

Key Benefits:

Quick Identification: See top issues at a glance
Prioritization: Focus on errors affecting multiple sessions
Pattern Recognition: Identify recurring problems

Understanding the Display:

Error Name: Type of error detected (e.g., ELEMENT NOT FOUND)
Session Count: Number of sessions affected

Example Use Case:

If "ELEMENT NOT FOUND" appears in 15 sessions, this indicates a UI locator issue that needs immediate attention across your test suite.

2. Build Performance

What is Build Performance?

Build Performance highlights recurring performance issues detected across analyzed sessions, helping you identify speed bottlenecks and UI responsiveness problems.

How it Works:

Monitors execution speed and response times
Identifies slow operations and delays
Detects UI flakiness and navigation issues
Aggregates performance flags from all sessions

Key Benefits:

Performance Monitoring: Track build-level performance trends
Bottleneck Detection: Identify slow operations
UX Issues: Spot flaky UI elements early
Optimization Opportunities: Find areas for improvement

Common Performance Flags:

slow_page_load: Pages taking longer than expected to load
flaky_ui_elements: UI elements behaving inconsistently
unused_navigation: Navigation steps that could be optimized
slow_api_responses: API calls exceeding response time thresholds

Example Use Case:

If "slow_api_responses" appears repeatedly, investigate API endpoints for optimization opportunities or increase timeout thresholds if appropriate.

3. Device Test Results/Device Analysed

What is Device Test Results?

Device Test Results shows pass/fail status for each device tested in your analyzed sessions, helping you identify device-specific issues and ensure cross-device compatibility.

How it Works:

Collects test results from all devices
Separates passed and failed devices
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Key Benefits:

Device Coverage: See which devices were tested
Compatibility Issues: Identify device-specific failures
Targeted Debugging: Focus on problematic devices

Example Use Case:

If "GOOGLE Pixel4XL" shows 3 failures in 3 session while other devices pass, investigate Android version compatibility, screen resolution issues, or device-specific APIs.

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