Abstract
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 analysis tasks. In this position paper we present a first example, adapting the NiCad code clone detector to identifying near-miss Simulink 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.
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