Software Engineering Institute

After examining why current approaches are inadequate, the SEI researchers examined what is needed to know whether a new approach works. They explore these factors: realistic data (class and feature distributions that relate to a transition domain), human participants (actual errors and learning patterns), and ground truth (because we know labelers are fallible).