Code Generation Approach Supporting Complex System Modeling based on Graph Pattern Matching

Abstract

Code generation is an effective way to drive the complex system development in model-based systems engineering. Currently, different code generators are developed for different modeling languages to deal with the development of systems with multi-domain. There are a lack of unified code generation approaches for multi-domain heterogeneous models. In addition, existing methods lack the ability to flexibly query and transform complex model structures to the target code, resulting in poor transformation efficiency. To solve the above problems, this paper proposes a unified approach which supports the code generation of heterogeneous models with complex model structure. First, The KARMA language based on GOPPRR-E meta-modeling approach is used for the unified formalism of models built by different modeling languages. Second, the code generation approach based on graph pattern matching is proposed to realize the query and transformation of complex model structures. Then, the syntax for code generation is integrated into KARMA and a compiler for code generation is developed. Finally, a case of unmanned vehicle system is taken to validate the effectiveness of the proposed approach.