Using mutation analysis for a model-clone detector comparison framework
Matthew Stephan, Manar H. Alalfi, Andrew Stevenson, James R. Cordy · 2013
Abstract—Model-clone detection is a relatively new area and there are a number of different approaches in the literature. As the area continues to mature, it becomes necessary to evaluate these approaches against each other and validate new ones that are introduced. We present a mutation-analysis based model-clone detection framework that attempts to automate and standardize the process of comparing multiple Simulink modelclone detection tools or variations of the same tool. By having such a framework, new research directions in the area of modelclone detection can be facilitated as the framework can be used to validate new techniques as they arise. We begin by presenting challenges unique to model-clone tool comparison including recall calculation, the nature of the clones, and the clone report representation. We propose our framework, which we believe will address these challenges. This is followed by a presentation of the mutation operators that we plan to inject into our Simulink models that will introduce variations of all the different model clone types that can then be searched for by each respective model-clone detector.