Towards an Industrial Stateful Software Rejuvenation Toolchain using Model Learning

Abstract

We present our vision for creating an industrial legacy software rejuvenation toolchain. The goal is to semi automatically remove code smells from stateful software used in Cyber Physical Systems (CPS). Compared to existing tools that remove code smells, our toolchain can remove more than one type of code smell. Additionally, our approach supports multiple programming languages because we use abstract models obtained by means of model learning. Supporting more than one programming language is often lacking in state of art refactoring tools.