Models are code too: Near-miss clone detection for Simulink models

While graph-based techniques show good results in finding exactly similar subgraphs in graphical models, they have great difficulty in finding near-miss matches. Text-based clone detectors, on the other hand, do very well with near-miss matching in source code. In this paper we introduce SIMONE, an adaptation of the mature text-based code clone detector NICAD to the efficient identification of structurally meaningful near-miss subsystem clones in graphical models. By transforming graph-based models to normalized text form, SIMONE extends NICAD to identify near-miss subsystem clones in Simulink models, uncovering important model similarities that are difficult to find in any other way.