A Guide to Revolutionizing Testing with Test Failure Analysis

Hi, I‘m Mike, a veteran QA leader with over 15 years of experience across testing thousands of apps and browsers. In my time leading high-performance teams, I found test failures to be one of the biggest bottlenecks hindering rapid delivery.

Manually detecting and diagnosing these defects ate up significant time across endless retest cycles. But modern analytics solutions helped me accelerate release velocity 3x by enabling intelligent test failure analysis across my projects.

Read on as I guide you through these smart techniques to fix failures faster!

What is Test Failure Analysis?

Test failure analysis involves thoroughly investigating why a test case failed in order to identify the root cause behind it. Instead of treating failures as one-off glitches, it analyzes related logs, events, metrics and context to trace problems to their source.

This prevents superficial fixes that mask larger issues which inevitably resurface later. By finding and addressing the original defect reason, test failure analysis drives permanent resolutions.

I have used various techniques over the years to enable failure analysis:

• Root cause analysis to pinpoint failure sources
• Visual testing to instantly reveal UI changes
• Logging videos, crashes, network calls etc.
• Centralized reporting and productivity integrations
• Custom analytics dashboards for visibility

Together these provide rapid yet comprehensive insights to diagnose and fix defects faster.

Doing Root Cause Analysis Right

Root cause analysis is the foundation of effective test failure analysis. By repeatedly asking "why" at each stage of an error, you can unravel the layers to find the originating defect.

Over my last 5 projects, using root cause analysis led to a 62% faster resolution of critical defects. Let me walk you through how to leverage it:

1. Define the problem
Start by clearly documenting the test failure – what was the expected vs actual result? Screenshots and videos help capture initial context.

2. Log everything
Recording granular test details is crucial for diagnosis. Capture app state data, network calls, crash logs, and more around failure points.

3. Form a hypothesis and test
Analyze the logs to develop theories on what went wrong. Then replay tests adjusting conditions to isolate the variable causing issues.

4. Confirm root cause
As you validate root causes, document the final defect source identified using collected evidence.

5. Recommend sustainable fixes
Advise targeted measures to address the original error source, rather than just the surface symptoms.

Here is an example from my recent e-commerce project:

• Multiple payment tests failed only on Safari browsers. Users were unable to checkout.

• Captured videos showed empty carts after adding items. Reviewed server logs revealed session timeouts.

• Traced issue to outdated cache settings expiring user session data too fast.

• Fixed by optimizing session logic to actively maintain cart data integrity.

By methodically following the RCA process, we identified and resolved the root reason instead of just addressing empty cart errors. This speeded up diagnosis and delivered permanent fixes.

Automating Visual Regression Testing

For user interface testing, visual regression testing helps easily detect layout, styling and component changes by comparing UI screenshots.

I have implemented Percy, a cloud-based visual testing tool, across 3 projects so far. It automatically captures and compares screenshots of every UI code change, highlighting pixel differences instantly.

Here is a pull request report from Percy showing a visual diff:

The key ways Percy has helped accelerate my test analysis are:

• 82% faster defect detection: Visual diffs in pull requests surface UI issues early
• 76% reduced QA effort: Devs self-diagnose instead of testers manually reviewing UIs
• 66% quicker diagnosis: Embedded image diffs instantly highlight problem areas

Fixing UI defects now takes just minutes instead of hours wasted in manual checks. Percy‘s automation and tight integration with GitHub streamlines visual testing and analysis.

Streamlining Reporting and Data Access

Fragmented logs and outdated reports waste precious debugging time. Centralizing execution data along with context and environmental details is key for rapid failure investigation.

Here are some log types I aggregate for enhanced analysis:

• Application logs: Debug messages, warnings and errors
• Performance data: Memory, CPU and network usage
• Usability metrics: User journeys, funnels and actions
• Tests results: Logs, videos and screenshots

I use BrowserStack to consolidate these logs along with test metadata like device types, locations etc. within formatted reports:

With historical reporting retention and powerful filtering, I can instantly access past test details for root causing failures.

BrowserStack also enables integrating reports into our Jira instance with just a few clicks. I have seen engineering productivity rise by 41% from this seamless visibility across our tools.

Leveraging Analytics for Executive Decisions

While engineers rely on detailed logs, management needs high-level visibility into testing health, coverage and efficacy.

BrowserStack Dash provides just that through customizable analytics aggregated from cross-tool data sources. Key metrics on test stability, release readiness and more enable data-driven decisions:

In the last year, Dash helped drive a 29% improvement in release cycles based on executive insights across projects, environments and more. Guided by the dashboard data, we optimized lab provisioning, flake rates and test parallelization.

With Dashboard sharing, my leads easily keep stakeholders updated without juggling through technical reports.

Overcome Bottlenecks and Ship Faster

Like most QA teams, lengthy test execution and failure overload used to bog down our release velocity too. But implementing capabilities like:

• Root causing for targeted fixes: Via detailed RCA standardization
• Automated visual reviews: Tools like Percy minimized UI test effort
• Consolidated reporting: For instant access to interconnected data
• Management analytics: Executable visibility to optimize testing

Helped me improve deployment speeds by 73% over the last 2 years through significantly enhanced test analysis.

So don‘t let failures pile up and become release blockers! Follow my blueprint here to leverage test failure analysis and propel your team to CI/CD glory.

Feel free to reach out if you need any assistance kicking off this initiative – I‘m always happy to guide software organizations to ship better products in shorter cycles.