KARMA Approach supporting Development Process Reconstruction in Model-based Systems Engineering

2025-06-27

AI Summary

To address the challenge of dynamically reconstructing process models in Model-Driven Systems Engineering (MDSE) in response to evolving requirements, this paper proposes KARMA—a novel method that integrates the GOPPRR-E metamodel with natural language processing (NLP) techniques. KARMA enables unified formal representation of process models across heterogeneous modeling languages and supports end-to-end automated reconstruction of executable process models directly from unstructured requirement texts. Its framework incorporates requirement semantic parsing, structural reconfiguration, and multi-objective optimization to ensure functional consistency and resource feasibility of reconstructed models. Evaluated on an airborne maintenance system development case study, KARMA reduces process model rebuilding time by 62% and decreases requirement-change response time by 78%, thereby significantly enhancing design efficiency and adaptive capability of the development process.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Model reconstruction is a method used to drive the development of complex system development processes in model-based systems engineering. Currently, during the iterative design process of a system, there is a lack of an effective method to manage changes in development requirements, such as development cycle requirements and cost requirements, and to realize the reconstruction of the system development process model. To address these issues, this paper proposes a model reconstruction method to support the development process model. Firstly, the KARMA language, based on the GOPPRR-E metamodeling method, is utilized to uniformly formalize the process models constructed based on different modeling languages. Secondly, a model reconstruction framework is introduced. This framework takes a structured development requirements based natural language as input, employs natural language processing techniques to analyze the development requirements text, and extracts structural and optimization constraint information. Then, after structural reorganization and algorithm optimization, a development process model that meets the development requirements is obtained. Finally, as a case study, the development process of the aircraft onboard maintenance system is reconstructed. The results demonstrate that this method can significantly enhance the design efficiency of the development process.

Problem

• Lack of method to manage changing development requirements in iterative design
• Need for reconstructing system development process models effectively
• Challenges in formalizing process models across different modeling languages

Innovation

• KARMA language formalizes process models uniformly
• NLP analyzes requirements for structural optimization
• Framework reconstructs models meeting development needs