Near-miss model clone detection for Simulink models

This paper describes our plan to adapt mature code-based clone detection techniques to the efficient identification of near-miss clones in models. Our goal is to leverage successful source text-based clone detection techniques by transforming graph-based models to normalized text form in order to capture semantically meaningful near-miss results that can help in further model analysis tasks. In this position paper we present a first example, adapting the NiCad code clone detector to identifying near-miss Simulink model clones at the “system” granularity. In current work we are extending this technique to the Simulink (entire) “model” and (more refined) “block” granularities as well.