Consistency of UML class, object and statechart diagrams using ontology reasoners

• URL: http://arxiv.org/abs/2205.11177v1
• Date: Mon, 23 May 2022 10:29:32 GMT
• Title: Consistency of UML class, object and statechart diagrams using ontology reasoners
• Authors: Ali Hanzala Khan, Ivan Porres
• Abstract summary: We propose an automatic approach to analyze consistency and satisfiability of Unified Modeling Language models containing multiple class, object and statechart diagrams. We describe how to translate models in OWL 2 and we present a tool chain implementing this translation that can be used with any standard compliant modeling tool.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We propose an automatic approach to analyze the consistency and satisfiability of Unified Modeling Language UML models containing multiple class, object and statechart diagrams using logic reasoners for the Web Ontology Language OWL 2. We describe how to translate UML models in OWL 2 and we present a tool chain implementing this translation that can be used with any standard compliant UML modeling tool. The proposed approach is limited in scope, but is fully automatic and does not require any expertise about OWL 2 and its reasoners from the designer.

Related papers

• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• Formalizing UML State Machines for Automated Verification -- A Survey [14.99225452541953]
  The Modeling Language (UML) is a standard for modeling dynamic systems. The specification, maintained by the Object Management Group (OMG), is documented in natural language.
  arXiv  Detail & Related papers  (2024-07-24T12:15:31Z)
• ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets [106.7760874400261]
  This paper presents ML-SUPERB2.0, which is a new benchmark for evaluating pre-trained SSL and supervised speech models. We find performance improvements over the setup of ML-SUPERB, but performance depends on the downstream model design. Also, we find large performance differences between languages and datasets, suggesting the need for more targeted approaches.
  arXiv  Detail & Related papers  (2024-06-12T21:01:26Z)
• PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4 [10.564949684320727]
  We introduce PathOCL, a novel path-based prompt augmentation technique designed to facilitate Object Constraint Language generation. Our findings demonstrate that PathOCL, compared to augmenting the complete class model (UML-Augmentation), generates a higher number of valid and correct OCL constraints.
  arXiv  Detail & Related papers  (2024-05-21T02:00:54Z)
• Model Generation with LLMs: From Requirements to UML Sequence Diagrams [9.114284818139069]
  This paper investigates the capability of ChatGPT to generate a specific type of model, i.e., sequence diagrams, from NL requirements. We examine the sequence diagrams generated by ChatGPT for 28 requirements documents of various types and from different domains. Our results indicate that, although the models generally conform to the standard and exhibit a reasonable level of understandability, their completeness and correctness with respect to the specified requirements often present challenges.
  arXiv  Detail & Related papers  (2024-04-09T15:07:25Z)
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs. We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
  arXiv  Detail & Related papers  (2024-01-14T16:17:07Z)
• Exploiting Contextual Target Attributes for Target Sentiment Classification [53.30511968323911]
  Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. We present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes.
  arXiv  Detail & Related papers  (2023-12-21T11:45:28Z)
• Conceptual Model Interpreter for Large Language Models [0.0]
  This paper applies code generation and interpretation to conceptual models. The concept and prototype of a conceptual model interpreter is explored. The results indicate the possibility of modeling iteratively in a conversational fashion.
  arXiv  Detail & Related papers  (2023-11-11T09:41:37Z)
• Coding by Design: GPT-4 empowers Agile Model Driven Development [0.03683202928838613]
  This research offers an Agile Model-Driven Development (MDD) approach that enhances code auto-generation using OpenAI's GPT-4. Our work emphasizes "Agility" as a significant contribution to the current MDD method, particularly when the model undergoes changes or needs deployment in a different programming language. Ultimately, leveraging GPT-4, our last layer auto-generates code in both Java and Python.
  arXiv  Detail & Related papers  (2023-10-06T15:05:05Z)
• ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models [74.64651681052628]
  We introduce ModelScope-Agent, a customizable agent framework for real-world applications based on open-source LLMs as controllers. It provides a user-friendly system library, with customizable engine design to support model training on multiple open-source LLMs. A comprehensive framework has been proposed spanning over tool-use data collection, tool retrieval, tool registration, memory control, customized model training, and evaluation.
  arXiv  Detail & Related papers  (2023-09-02T16:50:30Z)
• Metadata Representations for Queryable ML Model Zoos [73.24799582702326]
  Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the models. The metatada is currently not standardised; its expressivity is limited; and there is no way to store and query it. In this paper, we advocate for standardized ML model meta-data representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.
  arXiv  Detail & Related papers  (2022-07-19T15:04:14Z)

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.