2018 Hawaii false ballistic missile alert
A false warning of an inbound ballistic missile was broadcast to cellphones and TV/radio stations across Hawaii, triggering statewide panic amid heightened tensions with North Korea; no correction was issued for 38 minutes.
The damage was public. The root cause was preventable.
A false warning of an inbound ballistic missile was broadcast to cellphones and TV/radio stations across Hawaii, triggering statewide panic amid heightened tensions with North Korea; no correction was issued for 38 minutes.
What the software actually got wrong
During a drill, a warning officer selected a live-alert template from the alert system's drop-down menu and confirmed a 'send' prompt; investigators found the software gave no clear, fail-safe distinction between test and live alert templates and gave operators no pre-built way to quickly issue a retraction once a false alert had gone out, letting the error reach the public and go uncorrected for over half an hour.
During a drill, a warning officer selected a live-alert...
During a drill, a warning officer selected a live-alert template from the alert system's drop-down menu and confirmed a 'send' prompt
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.
Investigators found the software gave no clear, fail-safe distinction...
investigators found the software gave no clear, fail-safe distinction between test and live alert templates and gave operators no pre-built way to quickly issue a retraction once a false alert had gone out, letting the error reach the public and go uncorrected for over half an hour
Requs AI Software FMEA traces this failure mode back to the system-level hazard it feeds, tagging it against the Common Defect Enumeration so it surfaces in review instead of in the field.
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.
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.
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.