Mars Polar Lander loss
$165 million lander was lost, believed to have crashed on the Martian surface.
The damage was public. The root cause was preventable.
$165 million lander was lost, believed to have crashed on the Martian surface.
What the software actually got wrong
Vibration from leg deployment generated spurious sensor signals that flight software misinterpreted as touchdown, prematurely cutting the descent engines around 40 meters above the surface.
Vibration from leg deployment generated spurious sensor signals that...
Vibration from leg deployment generated spurious sensor signals that flight software misinterpreted as touchdown, prematurely cutting the descent engines around 40 meters above the surface.
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.
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.