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Case Study #28 — Top 100 Software Failures

Northeast Blackout of 2003

About 55 million people across the northeastern US and Ontario, Canada lost power, some for up to four days; estimated economic losses of $6 billion, and the blackout was linked to at least 11 deaths.

2003-08-14
When it happened
Energy / Utilities
Sector
#28 of 100
Ranked by documented impact
01 — What Happened

The damage was public. The root cause was preventable.

About 55 million people across the northeastern US and Ontario, Canada lost power, some for up to four days; estimated economic losses of $6 billion, and the blackout was linked to at least 11 deaths.

Energy
Sector affected
2003-08-14
Date of the event
#28
Rank in the Top 100 Software Failures
1
Distinct root-cause clause identified below
02 — The Root Cause

What the software actually got wrong

A race condition (software bug) in FirstEnergy's GE XA/21 energy management system caused the control room's alarm system to silently fail, so operators did not see warnings as transmission lines overheated and tripped in a cascading failure.

01Root Cause

A race condition (software bug) in FirstEnergy's GE XA/21...

What Happened

A race condition (software bug) in FirstEnergy's GE XA/21 energy management system caused the control room's alarm system to silently fail, so operators did not see warnings as transmission lines overheated and tripped in a cascading failure.

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.