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Case Study #38 — Top 100 Software Failures

AWS US-EAST-1 major outage

Widely used services including Disney+, Netflix, and Amazon's own logistics and Alexa systems were disrupted for hours during a major shopping period.

2021-12-07
When it happened
Cloud / Technology
Sector
#38 of 100
Ranked by documented impact
01 — What Happened

The damage was public. The root cause was preventable.

Widely used services including Disney+, Netflix, and Amazon's own logistics and Alexa systems were disrupted for hours during a major shopping period.

Cloud
Sector affected
2021-12-07
Date of the event
#38
Rank in the Top 100 Software Failures
1
Distinct root-cause clause identified below
02 — The Root Cause

What the software actually got wrong

An automated network-capacity scaling process triggered unexpected behavior in the internal network that manages core AWS services, causing congestion that cascaded into failures across multiple dependent services.

01Root Cause

An automated network-capacity scaling process triggered unexpected behavior in...

What Happened

An automated network-capacity scaling process triggered unexpected behavior in the internal network that manages core AWS services, causing congestion that cascaded into failures across multiple dependent services.

How Requs AI Catches This

Requs AI Edge Case flags this exact pattern at the requirements and architecture stage — before a single line of code implementing it exists — so the assumption behind it gets challenged while it is still cheap to fix.

03 — How This Gets Caught Before It Happens

Beyond code coverage and "shall" testing

Root causes like this one rarely show up in code coverage or requirements-compliance testing, because nobody wrote a requirement anticipating the specific edge case that broke. Requs AI Edge Case and Requs AI Software FMEA are built to surface exactly this class of overlooked failure mode — before the software is written.

Requs AI Edge Case

Surfaces this before code exists

Identifies edge cases like this one at requirements and architecture time, using the Common Defect Enumeration to catalog failure patterns seen across hundreds of real-world software failures — including this one.

Requs AI Software FMEA

Connects the failure mode to the hazard

Traces this class of root cause directly to the system-level hazard it can produce, so a defect pattern like this one gets flagged during design review instead of after it ships.

Find the overlooked root causes before they ship.

Schedule a demonstration, or explore how Requs AI Edge Case and Software FMEA use the Common Defect Enumeration.