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Case Study #184 — 300 Software Failure Case Studies

AT&T nationwide network outage

AT&T customers across the US lost cellular service, including the ability to make emergency calls, for much of a business day.

2024-02-22
When it happened
Telecommunications
Sector
#184 of 300
Ranked by documented impact
01 — What Happened

The damage was public. The root cause was preventable.

AT&T customers across the US lost cellular service, including the ability to make emergency calls, for much of a business day.

Telecommunications
Sector affected
2024-02-22
Date of the event
#184
Rank in the 300 Software Failure Case Studies
1
Distinct root-cause clause identified below
02 — The Root Cause

What the software actually got wrong

AT&T said the outage resulted from the application and execution of an incorrect process used while attempting to expand network capacity, not a cyberattack, which caused connected devices to lose their ability to connect to the network.

01Root Cause

AT&T said the outage resulted from the application and...

What Happened

AT&T said the outage resulted from the application and execution of an incorrect process used while attempting to expand network capacity, not a cyberattack, which caused connected devices to lose their ability to connect to the network.

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.