Diagnosing Stream-Mined Model Changes of Monitored Requirements for Cognitive Rehabilitation

Personalized requirements practices can be applied to specify goals as part of a clinical plan to aid cognitive rehabilitation. In this context, requirements monitoring can aid clinicians in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models can identify potential changes in user goal attainment, as a user learns his or her personalized emailing system. When the quality of some models varies significantly from nearby models-as defined by quality metrics-then the user's behavior is automatically flagged as a potentially significant behavioral change. The specific changes in user behavior can be automatically derived by differencing the data-mined decision-tree model. This paper describes how decision tree differencing can aid diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The technique may be more widely applicable to other requirements monitoring contexts.