arXiv subjects

Markus Stumptner

Publications and source records attributed to Markus Stumptner.

6 recordsLinked to original sources

arXiv2026

Can LLMs Learn and Apply Multi-Level Modelling Semantics? A First Empirical Study

Industry 5.0 emphasises human-centric industrial system design, placing additional demands on modelling tools. Multi-level modelling (MLM) can directly represent three or more abstraction levels, but this comes at the cost of more complex semantic constraints that model correctness depends on. Large Language Models (LLMs) have been increasingly studied in model-driven engineering, but this evidence rests entirely on two-level modelling tasks, and whether it generalises to MLM, whose semantics differ in kind, remains untested. This paper presents the first empirical study of this question. We have three commercial LLMs (GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro) generate multi-level models for the MULTI Warehouse Challenge in the SLICER language under six prompting strategies, yielding 90 generated models compared against a manually validated reference using fourteen metrics. Syntactic correctness is within reach, but semantic correctness is only partially achieved, with Instantiation/Specialisation Correctness ranging from 52% to 79%. Models reproduce content stated explicitly in the task text, but rarely complete structure and constraints the text implies without stating. Prompting strategies trade off precision against completeness, and self-checking functions mainly as a rule checker rather than reliably improving alignment with the reference design. Among the three LLMs, Claude shows the most balanced profile. These results clarify the boundaries of current LLM capability for MLM and inform the design of human-centred, AI-assisted modelling workflows for Industry 5.0.

cs.SE↗

arXiv2026

Two-Level vs. Multi-Level Modelling: An Empirical Study of Cascading Maintenance Burden

When a core definition changes, every dependent artefact must be updated, a cascading problem central to software maintenance. In Model-Driven Engineering (MDE), the dominant two-level modelling (2LM) paradigm fragments domain knowledge across metamodel and model artefacts that must be kept mutually consistent, making co-evolution a persistent source of inconsistencies and effort. Multi-level modelling (MLM) unifies these artefacts and is claimed to reduce co-evolution burden, but this has not been tested in a controlled, paired comparison against 2LM. We hypothesise that MLM's structural unification yields fewer post-change inconsistencies and a smaller modification footprint than 2LM for semantically equivalent evolution scenarios. To test this, we present a pre-registered, mutation-based empirical comparison of co-evolution behaviour in both paradigms. From a curated corpus of published 2LM co-evolution scenarios, we construct semantically equivalent MLM counterparts, apply identical evolution mutations to both, and measure outcomes through automated consistency checking and pre-registered hypothesis tests. Positive controls and a blinded mapping protocol guard against bias. This design provides the first empirical framework for assessing whether paradigm-level structural choices affect cascading maintenance burden, operationalising co-evolution burden as two automatically measurable outcome variables and delivering a reusable benchmarking protocol for replication and extension.

cs.SE↗

arXiv2024

Iof-maint -- Modular maintenance ontology

In this paper we present a publicly-available maintenance ontology (Iof-maint). Iof-maint is a modular ontology aligned with the Industrial Ontology Foundry Core (IOF Core) and contains 20 classes and 2 relations. It provides a set of maintenance-specific terms used in a wide variety of practical data-driven use cases. Iof-maint supports OWL DL reasoning, is documented, and is actively maintained on GitHub. In this paper, we describe the evolution of the Iof-maint reference ontology based on the extraction of common concepts identified in a number of application ontologies working with industry maintenance work order, procedure and failure mode data.

cs.AI↗

arXiv2017

An Architecture for Establishing Legal Semantic Workflows in the Context of Integrated Law Enforcement

Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is a solution that is inflexible, incurs in high costs, leads to "silos" that prevent sharing data across different agencies or tasks, and is unable to cope with the modern environment, where workflows, tasks, and priorities frequently change. Operating within the Data to Decision Cooperative Research Centre (D2D CRC), the authors are currently involved in the Integrated Law Enforcement Project, which has the goal of developing a federated data platform that will enable the execution of integrated analytics on data accessed from different external and internal sources, thereby providing effective support to an investigator or analyst working to evaluate evidence and manage lines of inquiries in the investigation. Technical solutions should also operate ethically, in compliance with the law, and subject to good governance principles.

cs.CY↗

arXiv2003

Model-Based Debugging using Multiple Abstract Models

This paper introduces an automatic debugging framework that relies on model-based reasoning techniques to locate faults in programs. In particular, model-based diagnosis, together with an abstract interpretation based conflict detection mechanism is used to derive diagnoses, which correspond to possible faults in programs. Design information and partial specifications are applied to guide a model revision process, which allows for automatic detection and correction of structural faults.

cs.SE↗

arXiv2000

Extended Abstract - Model-Based Debugging of Java Programs

Model-based reasoning is a central concept in current research into intelligent diagnostic systems. It is based on the assumption that sources of incorrect behavior in technical devices can be located and identified via the existence of a model describing the basic properties of components of a certain application domain. When actual data concerning the misbehavior of a system composed from such components is available, a domain-independent diagnosis engine can be used to infer which parts of the system contribute to the observed behavior. This paper describes the application of the model-based approach to the debugging of Java programs written in a subset of Java. We show how a simple dependency model can be derived from a program, demonstrate the use of the model for debugging and reducing the required user interactions, give a comparison of the functional dependency model with program slicing, and finally discuss some current research issues.

cs.SE↗