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

• URL: http://arxiv.org/abs/2506.22037v1
• Date: Fri, 27 Jun 2025 09:34:08 GMT
• Title: KARMA Approach supporting Development Process Reconstruction in Model-based Systems Engineering
• Abstract summary: This paper proposes a model reconstruction method to support the development process model.<n>The KARMA language, based on the GOPPRR-E metamodeling method, is utilized to uniformly formalize the process models.<n>As a case study, the development process of the aircraft onboard maintenance system is reconstructed.
• Score: 16.45548088648068
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models [62.62160094849002]
  RADIANT is an engineering methodology that combines MDE with Multi-Agent Large Language Models.<n>It automatically generates heterogeneous models across engineering phases.<n>It provides exact, automated change-impact analysis.
  arXiv  Detail & Related papers  (2026-07-18T08:50:55Z)
• Application of machine learning for infrastructure reconstruction programs management [0.0]
  This article describes an adaptive decision-making support model aimed at improving efficiency of engineering infrastructure reconstruction program management.<n>The main components of the model are defined, which include a set of decision-maker preferences, decision-making tasks, sets of input data, and applied software components.<n>The application of the developed adaptive model is possible in the management of programs for the reconstruction of such engineering systems as systems of heat, gas, electricity supply, water supply, and drainage, etc.
  arXiv  Detail & Related papers  (2025-11-25T23:14:36Z)
• Step-Aware Policy Optimization for Reasoning in Diffusion Large Language Models [57.42778606399764]
  Diffusion language models (dLLMs) offer a promising, non-autoregressive paradigm for text generation.<n>Current reinforcement learning approaches often rely on sparse, outcome-based rewards.<n>We argue that this stems from a fundamental mismatch with the natural structure of reasoning.
  arXiv  Detail & Related papers  (2025-10-02T00:34:15Z)
• Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems [2.9297240479517836]
  We propose a unified three-phase framework that integrates process-based models with machine learning (ML)<n>Phase I, behavioral distillation, enhances process models via surrogate learning and model simplification.<n>Phase II, structural distillation, reformulates process equations as modular components within a graph neural network (GNN)<n>Phase III, cognitive distillation, embeds expert reasoning and adaptive decision-making into intelligent modeling agents.
  arXiv  Detail & Related papers  (2025-09-02T05:24:35Z)
• A Survey of Model Architectures in Information Retrieval [64.75808744228067]
  We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• Expanding the Classical V-Model for the Development of Complex Systems Incorporating AI [13.059865395440603]
  We present an extension of the V-model for iterative data-based development processes.<n>The iterative approach allows for seamless integration of continuous system refinement.<n>While the data-based approach constitutes the consideration of data-based development processes.
  arXiv  Detail & Related papers  (2025-02-18T11:01:37Z)
• Generalized Factor Neural Network Model for High-dimensional Regression [50.554377879576066]
  We tackle the challenges of modeling high-dimensional data sets with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships.<n>Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression.
  arXiv  Detail & Related papers  (2025-02-16T23:13:55Z)
• STAR: Synthesis of Tailored Architectures [61.080157488857516]
  We propose a new approach for the synthesis of tailored architectures (STAR)<n>Our approach combines a novel search space based on the theory of linear input-varying systems, supporting a hierarchical numerical encoding into architecture genomes. STAR genomes are automatically refined and recombined with gradient-free, evolutionary algorithms to optimize for multiple model quality and efficiency metrics.<n>Using STAR, we optimize large populations of new architectures, leveraging diverse computational units and interconnection patterns, improving over highly-optimized Transformers and striped hybrid models on the frontier of quality, parameter size, and inference cache for autoregressive language modeling.
  arXiv  Detail & Related papers  (2024-11-26T18:42:42Z)
• Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
  Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based optimization (MBO) problems.<n>We present Cliqueformer, a transformer-based architecture that learns the black-box function's structure through functional graphical models (FGM)<n>Across various domains, including chemical and genetic design tasks, Cliqueformer demonstrates superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-10-17T00:35:47Z)
• A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model [25.78239568393706]
  This paper introduces a taxonomy focused on the architectures of foundation-model-based agents. By unifying and detailing these classifications, our taxonomy aims to improve the design of foundation-model-based agents.
  arXiv  Detail & Related papers  (2024-08-06T03:10:52Z)
• Process Modeling With Large Language Models [42.0652924091318]
  This paper explores the integration of Large Language Models (LLMs) into process modeling. We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models. Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
  arXiv  Detail & Related papers  (2024-03-12T11:27:47Z)
• Methodology for Holistic Reference Modeling in Systems Engineering [0.0]
  This paper presents a holistic approach to describe reference models across different views and levels. Benefits include an end-to-end traceability of the capability coverage with performance parameters considered already at the starting point of the reference design.
  arXiv  Detail & Related papers  (2022-11-21T13:41:07Z)
• Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
  The paper presents an approach to process mining providing semi-automatic support to model optimization. A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity. We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
  arXiv  Detail & Related papers  (2022-06-10T16:20:59Z)
• Dynamically Grown Generative Adversarial Networks [111.43128389995341]
  We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
  arXiv  Detail & Related papers  (2021-06-16T01:25:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.