2005 SAT scoring errors (Pearson Educational Measurement)
Pearson Educational Measurement's scanning of SAT answer sheets incorrectly scored thousands of students' October 2005 tests, some scores too low, affecting college admissions decisions already made on the erroneous scores.
The damage was public. The root cause was preventable.
Pearson Educational Measurement's scanning of SAT answer sheets incorrectly scored thousands of students' October 2005 tests, some scores too low, affecting college admissions decisions already made on the erroneous scores.
What the software actually got wrong
The College Board found that dampness had caused some answer sheets to expand slightly, and the automated optical scanning equipment and software used to read the sheets misread some answers as a result, an error that went undetected until an unusual number of students requested hand rescoring.
The College Board found that dampness had caused some...
The College Board found that dampness had caused some answer sheets to expand slightly
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.
The automated optical scanning equipment and software used to...
the automated optical scanning equipment and software used to read the sheets misread some answers as a result, an error that went undetected until an unusual number of students requested hand rescoring
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.