# Modeling and Digital Twins: Insights and Strategies for Software Engineers

Silvia Abrahão, Miroslaw Staron, Judith Michael, Benoît Combemale, Marsha Chechik

► To cite this version:

Silvia Abrahão, Miroslaw Staron, Judith Michael, Benoît Combemale, Marsha Chechik. Modeling and Digital Twins: Insights and Strategies for Software Engineers. IEEE Software, 2025. hal-05485243

HAL Id: hal-05485243

https://inria.hal.science/hal-05485243v1

Submitted on 30 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# IEEE Software Practitioner's digest

March-April 2025 Issue

**Column title:** Modeling and Digital Twins: Insights and Strategies for Software Engineers

**Authors:** Silvia Abrahão, Miroslaw Staron, Judith Michael, Benoit Combemale and Marsha Chechik

Using software modeling to address complex systems offers several significant benefits, enabling engineers to design, manage, and evolve such systems more effectively. However, digital twins transform how software engineers design, operate, and maintain complex systems, fostering innovation, efficiency, and reliability in diverse industries. Despite their potential, adopting this conceptual framework poses significant challenges for software engineers, including technical and organizational hurdles and the complexity of integrating digital twin engineering into existing workflows.

To provide practical insights, this column discusses challenges and highlights approaches discussed at the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems (MODELS 2024) and the 1st International Conference on Engineering Digital Twins (EDTconf 2024). The selected papers showcase advancements in the engineering of complex software systems, emphasizing modeling techniques and digital twins to address challenges such as improving system understanding and stakeholder communication, enhancing design and development processes, optimizing lifecycles, and supporting the integration of complex systems. We welcome your feedback and suggestions on the topics covered. Additionally, if you experiment with or adopt any of the practices featured in this column, please share your experiences with us and the authors of the respective papers.

## Deriving UML Sequence Diagrams from User Stories

User stories play a key role in agile software development as they help teams focus on delivering value to the customer by defining features or requirements from the user's perspective. However, their inherent ambiguities can lead to miscommunication and other challenges during software development. In the paper "Automated Derivation of UML Sequence Diagrams from User Stories: Unleashing the Power of Generative AI vs. a Rule-Based Approach," Munima Jahan, Mohammad Mahdi Hassan, Reza Golpayegani, Golshid Ranjbaran, Chanchal Roy, Banani Roy, and Kevin Schneider, address the challenge of generating Unified Modeling Language (UML) sequence diagrams from natural language user stories. Specifically, the paper proposes an automated solution using both a rule-based approach and Large Language Models (LLMs), such as ChatGPT, to generate sequence diagrams from these stories.

The rule-based approach starts with a preprocessing phase to extract noun phrases from a text file containing a list of user stories. The authors then define and apply a set of natural language processing (NLP) heuristics to identify the key elements of a sequence diagram (sender, receiver, and messages). These elements are refined in subsequent steps to produce the final
diagrams. The LLM-based approach employs ChatGPT 3.5 in a zero-shot scenario to generate sequence diagrams in PlantUML script format. This approach leverages the generative capabilities of ChatGPT to process and interpret user stories directly, bypassing the need for manual heuristics.

The authors conducted experiments using 100 user stories from two benchmark datasets, comparing the performance of their rule-based method with that of ChatGPT. They discuss several implementation challenges encountered during the process of generating UML sequence diagrams from user stories: 1) natural language complexity (parsing and accurately interpreting the structure of user stories, particularly when dealing with compound nouns and actions, posed a challenge); 2) sequence-structure variability (the variability in sentence structures in user stories required the rule-based approach to implement diverse heuristics to handle a wide range of sentence formats); 3) handling of compound sentences (breaking compound sentences into separate stories demanded additional preprocessing to ensure accurate diagram generation) and 4) ambiguity in user stories (ChatGPT's ability to infer context helped resolve some ambiguities but occasionally introduced extraneous complexity, deviating from the simplicity of the original user stories).

The findings revealed that the rule-based approach effectively generated simpler, more accurate diagrams for straightforward user stories. At the same time, ChatGPT produced more detailed diagrams but with increased complexity, sometimes going beyond the original simplicity of the user stories. This research improves software design processes by demonstrating how AI can support requirements engineering and model generation. The paper was presented at the *ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems* and is available at [https://doi.org/10.1145/3640310.3674081](https://doi.org/10.1145/3640310.3674081).

## Testing LLM-based systems for Fairness and Bias

Large language models (LLMs) are increasingly integrated into software systems. However, LLMs may reflect a biased behavior, resulting in systems that could discriminate against gender, age, or ethnicity, among other ethical concerns. Society and upcoming regulations aim to force companies and development teams to ensure their AI-enhanced software is ethically fair. The paper "A DSL for Testing LLMs for Fairness and Bias" by Sergio Morales, Robert Clarisó, and Jordi Cabot introduces LangBiTe, a domain-specific language (DSL) for specifying ethical requirements and automating the testing of biases in LLMs. The approach allows developers and non-technical users to specify ethical concerns and generate and execute test cases to evaluate LLM outputs against ethical requirements. The model-driven nature of the approach makes specifying requirements and generating tests platform-independent, providing traceability between the requirements and their assessment.

Some ethical concerns the authors consider include gender, sexual orientation, race, age, nationality, religion, and politics. The list can be extended to include further ethical issues. The approach is implemented in a tool that (1) assists users in defining an ethical requirements model and its test scenarios, (2) automatically generates and executes the test cases against the LLMs of their choice, and (3) reports the insights derived from the test results.
The paper illustrates the approach's applicability using a case study developed by the Luxembourg Institute of Science and Technology (LIST), which leveraged LangBiTe to build an LLM leaderboard that specialized in ethical bias evaluation. The leaderboard development team defined seven ethical requirements addressing 41 sensitive communities and a single test scenario. The testing system could automatically generate 316 test cases, connect to 16 LLMs, and execute the tests.

The results showed that all targeted LLMs exhibited some bias (mostly for politics).
Consequently, the authors suggest that if an organization faces this scenario when selecting an LLM to integrate within its in-development product, it would have to decide either to fine-tune some of the existing open-source models, train a new LLM that fulfills its ethical requirements, renegotiate the requirements, or use a model that performs better for their most pressing ethical concerns. The evaluation's key findings indicate that the tool can raise awareness about biases in LLM-based systems. The paper also highlights the importance of continuous testing, given that LLM updates could silently introduce new biases.

This research was presented at the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems and is available at https://dl.acm.org/doi/10.1145/3640310.3674093. The tool is available as an open-source resource on GitHub for broader community use.

## Model Federation Challenges for Software Systems

Integrating heterogeneous domains early in the development process is crucial in the design of complex software systems. Model federation offers a powerful approach to addressing various software engineering challenges by enabling semantic interoperability among diverse models. It achieves this by providing mechanisms to reify correspondences between different model elements, incorporate custom behaviors, and bridge gaps across technological spaces. While this technology has been used in industry, several challenges persist, and further insights are needed to address them effectively.

The paper "10 years of Model Federation with Openflexo: Challenges and Lessons Learned" by Jean-Christophe Bach, Antoine Beugnard, Joel Champeau, Fabien Dagnat, Sylvain Guérin, and Salvador Martínez reflects on the authors' decade-long experience with the development and application of OpenFlexo. This tool manages semantic interoperability across heterogeneous models and technological spaces. The paper explores the context, ambitions, and challenges that led to the creation of the tool, delving into the authors' vision of model federation. It also presents insights from five real-world use cases drawn from industrial and academic contexts to highlight lessons learned.

The authors identify challenges such as ensuring seamless integration with existing practices, treating any artifact as a model, and adopting a systemic approach. From the tool's various applications, they derive lessons learned, including the need for robust engineering
abstractions, the difficulty of the tool maintenance and dissemination in an academic context, the need for different concrete syntaxes and blended modeling techniques, the importance of an approach for structuring the federation of models, the adaptability of the tool to diverse use cases, and the necessity of mechanisms for model reuse. The paper concludes by presenting a roadmap to tackle the identified challenges, focusing on the collaboration among diverse stakeholders in developing complex systems, engaging domain modeling experts, and applying model federation techniques to advance the field of digital twins. The paper was presented at the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems and is available at https://dl.acm.org/doi/10.1145/3640310.3674084.

## Modeling Languages for Automotive Digital Twins

The demand for digital twins and suitable modeling techniques in the automotive industry is increasing rapidly. Yet, there is no common understanding of digital twins within the automotive sector, nor are there standardized modeling techniques established for creating automotive digital twins. Recent studies on digital twins have primarily focused on analyzing the literature in general but often overlook the industrial perspective of automotive practitioners.

To bridge this gap between scientific literature and industry practices, the paper "Modeling Languages for Automotive Digital Twins: A Survey Among the German Automotive Industry" by Jérôme Pfeiffer, Dominik Fuchß, Thomas Kühn, Robin Liebhart, Christer Neimöck, Christian Seiler, Anne Koziolek, and Andreas Wortmann, reports the results of a questionnaire survey conducted among experts in the German automotive industry. The survey aimed to identify i) the desired purposes and capabilities of digital twins and ii) the modeling techniques used in the engineering and operation of digital twins throughout the various phases of automotive development.

In the automotive industry, digital twins are currently considered the most useful in automotive development's usage and support phase. This is supported by the fact that most participants believe the digital twin should describe the vehicle as it is operated. According to the survey, the digital twin's primary functions are data monitoring, validations, and predicting future behavior. However, the purpose of the digital twin varies depending on the phase of automotive development. In the early phases, it should assist with validation, while in later phases, it should be used for risk assessment, particularly from the production phase onward.

Concerning the modeling techniques employed, simulation models, source code, and business process models were identified as the most frequently used. Given that various types of models are relevant at different phases of automotive development, the tools used to create these models are also critical. The paper concludes that there is a need to integrate and interact with multiple modeling languages and tools when developing digital twins in the automotive domain. The paper was presented at the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems and is available at https://dl.acm.org/doi/10.1145/3640310.3674100.

## Integrating Product and Production Digital Twins in the Automotive Domain
The manufacturing sector remains one of the largest application domains for digital twin engineering, particularly in the manufacturing industry. Digital twins in this context often represent various facets of the production environment, such as complex production systems, manufactured products, or manufacturing processes. Representing these aspects in digital twins often leads to either a product-focused or a production facility-focused perspective. However, large-scale automotive manufacturing facilities necessitate the integration of both perspectives. In the paper "An Architecture for the Integration of Product and Production Digital Twins in the Automotive Industry", Ryno Visser, Anton Basson, and Karel Kruger, introduce an architecture for a digital twins system that integrates product and production data in automotive manufacturing.

The proposed architecture includes several key components for management services: the service network, a gateway service, source adapters, dashboards, an API service, and the digital twin hierarchy. The digital twin hierarchy mirrors the real-world system hierarchy through a hierarchical decomposition of physical system components. Each digital twin in this hierarchy features an internal architecture comprising services for data ingestion, request handling, data warehousing, federation, and conformity. Data is stored in warehouses or federation logs, and the digital twin can store different *models* for various services.

To improve scalability for large systems, the proposed architecture relies on the concept of "type digital twin aggregates", which describes the aggregation of instances of the same digital twin type. For example, after a car's manufacturing is complete, the type of digital twin aggregates can provide services for all the associated instances, reducing computational resource demands.

A preliminary evaluation with a major automotive manufacturer demonstrates that this architecture meets critical functional and non-functional requirements. In the automotive manufacturing industry, key categories for digital twin hierarchies include 1) facility digital twins (representing entire factories such as a vehicle assembly factory or a cell within the factory); 2) resource digital twins (covering manufacturing equipment such as spot-welding robots or more advanced applications such as humans working in a facility); 3) executed process digital twins (documenting processes like welding, assembly, or charging), and 4) product digital twins (representing components such as engines, high-voltage batteries, gearbox, or the entire vehicle).

The architecture facilitates cross-level integration by enabling process models to query information about specific manufacturing steps and the resources involved in producing a product. The paper was presented at the *1st International Conference on Engineering Digital Twins* and is available at [https://dl.acm.org/doi/10.1145/3652620.3688257](https://dl.acm.org/doi/10.1145/3652620.3688257).

## Developing Digital Twins at Large Scale

Building Digital Twins for large-scale, smart, and critical infrastructures—such as power grids, water systems, and transportation networks—is a complex endeavor. These digital twins must integrate large amounts of heterogeneous data from diverse data sources, including (e.g., weather data and consumption/production data from smart meters) and data from third-party
systems like geographical information systems (GIS), enterprise resource planning (ERP), and
supervisory control and data acquisition (SCADA) tools. The scale is staggering, often involving
hundreds of billions of data points. Moreover, these systems demand high performance, as
delays or slow reactions could lead to physical damage. In their paper “GreyCat: A Framework
to Develop Digital Twins at Large Scale” Francois Fouquet, Thomas Hartmann, Cyril Cecchinel,
and Benoit Combemale present a robust framework to overcome these challenges. GreyCat
efficiently integrates and processes data from diverse sources, supporting the development of
digital twins that operate over large-scale digital shadows.

GreyCat relies on a foundation of imperative programming, graph-based databases (with nodes
serving as containers for properties), and scalable memory management. The framework
employs an executable language to define types, attributes, and operations for and operations
for traversing and mutating graph nodes with an imperative syntax. These specifications are
compiled into a program deployed and executed within a GreyCat runtime instance. To interact
with this program, GreyCat supports multiple transport protocols, including HTTP(S), MQTT,
Kafka, and OPC-UA. A standout feature of GreyCat is its ability to handle datasets larger than
the available memory. This is achieved by automatically managing memory-to-disk mapping,
ensuring seamless scalability. Its digital twin services include monitoring, deviation detection,
and performing “what-if” simulations for predictive analysis.

The paper showcases the use of this framework through two examples: a digital twin for tree
health monitoring and activation of water devices in harsh urban areas and Alva, the national
digital twin of Luxembourg’s power grid, which manages billions of data points in operation. The
first example demonstrates GreyCat’s suitability for IoT-driven systems with real-time feedback.
The second example is a large-scale digital twin that integrates over 50 billion time series data
points from over 350.000 smart meters. It continuously computes power flow across
approximately 320.000 cables and monitors tens of thousands of grid elements in real time.
With more than 1.2 million distributed machine learning models, Alva profiles and forecasts
energy consumption and production accurately and quickly.

These use cases illustrate GreyCats' ability to handle billions of data points with low latency and
high computational performance, making it an effective tool for managing large-scale digital twin
systems. The paper was presented at the 1st International Conference on Engineering Digital
Twins and is available at https://dl.acm.org/doi/10.1145/3652620.3688265. GreyCat
development environment and documentation can be downloaded at https://greycat.io.

About the authors:

**Silvia Abrahão** is a full professor at the Department of Computer Science, Universitat Politècnica de València, Valencia, 46022, Spain. Contact her at [https://sabrahao.wixsite.com/dsic-upv](https://sabrahao.wixsite.com/dsic-upv) or [sabrahao@dsic.upv.es](mailto:sabrahao@dsic.upv.es).

**Miroslaw Staron** is a full professor in the Interaction Design and Software Engineering division in the Computer Science and Engineering Department shared by Chalmers University of
Technology and the University of Gothenburg, Sweden, 412 96 Sweden. Contact him at
[https://www.staron.nu](https://www.staron.nu) or [miroslaw.staron@cse.gu.se](mailto:miroslaw.staron@cse.gu.se).

**Judith Michael** is PostDoc and team leader at the Chair for Software Engineering of RWTH Aachen University, 52062 Aachen, Germany. Contact her at [https://www.se-rwth.de/](https://www.se-rwth.de/) or [michael@se-rwth.de](mailto:michael@se-rwth.de).

**Marsha Chechik** is a full professor in the Department of Computer Science, University of Toronto, Canada. Contact her at [chechik@cs.toronto.edu](mailto:chechik@cs.toronto.edu).

**Benoit Combemale** is a full professor in the Department of Computer Science, University of Rennes, France. Contact him at [http://combemale.fr](http://combemale.fr) or [benoit.combemale@irisa.fr](mailto:benoit.combemale@irisa.fr).