The usage of generative AI in the software engineering domain could be a beneficial tool. All phases of the software development can be supported. However, there are also different challenges that can occur. Based on empirical studies we examined and validated the usage of Generative AI in software engineering. We derived possible use cases, limitations and future research opportunities.

The circular economy (CE) is an economic philosophy aimed at eliminating waste and the continual use of resources. It emphasizes designing products for longevity, reuse, and recycling to create a closed-loop system. One of the goals of enterprise modeling (EM) is to support enterprises in change processes from the current situation into a desired future state. In the context of the transition to the CE, the question arises if and how EM methods and languages must be adapted for the CE. The main contributions of this work are a better understanding of the challenges manufacturing enterprises face when preparing their product architectures for the CE, a meta-model preparing the product perspective in EM methods for CE, a QFD-based method component guiding the translation of CE goals to product properties and modules, and an investigation of what changes are required in EM methods. The work uses real-world case studies to motivate the research, identify requirements, and evaluate the findings.

Enterprise Architecture (EA) aims to align IT with business goals, but the concept of “future viability” remains academically abstract and practically elusive, creating a significant theory-practice gap. This study addresses this gap through a qualitative content analysis of semi-structured interviews with seasoned EA practitioners. Our findings reveal that practitioners define future viability not as a technical property, but as a direct outcome of business strategy, making its assessment highly context-dependent. While confirming that business capabilities serve as a “central anchor point” for translating strategy into design, the analysis also shows that the primary obstacles are not technical but organizational: the significant impact of technical debt and pervasive institutional inertia. By empirically validating and nuancing theoretical concepts, this paper contributes a practitioner-grounded understanding of future viability, offering actionable insights into the real-world challenges and strategic imperatives for building resilient and adaptable enterprise architectures.

Large language models (LLMs) are used in many fields today, including medicine, education, finance, and engineering. The quality of output generated by LLMs is a crucial factor for their successful application. Recent work has shown that LLMs can be a productive support in enterprise modeling (EM) for modelers and domain experts in modeling the current situation of enterprises. In this paper, we explore the capabilities of LLMs to control the quality of LLM-generated output in the context of EM. We utilize three different LLMs to evaluate their own output (self-validation) and that of the other LLMs (cross-validation). The main contributions of this paper are (1) an approach for using LLMs for quality control of LLMs in EM, including quality criteria and their operationalization, and (2) quasi-experiments demonstrating the applicability of the approach. The results confirm that our approach can be applied to the defined scenarios and demonstrate that cross-validation yields more comprehensive and reliable outputs than self-validation. Combining multiple LLMs leads to clearer, more trustworthy results, allowing for greater confidence in LLM usage in EM.

In many areas of conceptual modeling, large language models (LLMs) can be applied as assistive technology, for example, to gather domain knowledge to support model creation or to generate models from text. However, there is limited work on using LLMs to support enterprise architecture (EA) modeling. An LLM-based approach for generating EA models from text must consistently incorporate the different architectural layers of an EA. The aim of this paper is to contribute to this area by analyzing existing research, designing an approach to generating LLM-based models from text, and evaluating it experimentally. More concretely, an LLM-based approach designed for enterprise models is adapted for EA modeling with ArchiMate. The results of the experiments not only confirm feasibility but also a decent level of model quality. A set of recommendations for practical LLM use in EA modeling is abstracted, and implications for future research are derived.

Large language models (LLMs) have been found to be a support for modeling tasks in various application areas, including enterprise modeling (EM). In EM, LLMs can be applied to help domain experts create models efficiently that adhere to the correct syntax of the modeling language. In this context, how to organize the interplay of the domain expert and LLM is an important topic. Should the domain expert get an LLM-generated model and improve it (LLM-first) or should LLMs be used to improve models developed by domain experts (domain expert-first)? The paper investigates the interplay between domain expert and LLM by investigating three different application examples and conducting quasi-experiments. The results also contribute to determining the potential and limits of LLMs in EM.

The interest in digital twins (DTs) has recently increased among various industries. Consequently, various methods have been proposed to enhance their development and maintenance. The creation of DTs relies on integrating models and data from a variety of sources, including sensors, Internet of Things (IoT) devices, and other data streams. Model-driven engineering (MDE) is considered an effective approach for developing DTs. Our previous research indicates that current methods focus primarily on the software and systems perspective, often overlooking the importance of organizational integration. Additionally, we identified a growing need for approaches specifically designed to meet the needs of small and medium-sized enterprises (SMEs). In this paper, we introduce a method for MDE of DTs with the corresponding tool support meeting the specific requirements of SMEs. In three case studies from different domains, we demonstrate the applicability and confirm that domain experts are able to develop DTs with domain knowledge using our method.

In contrast to traditional business models, digital business ecosystems (DBEs) have several distinctive features—heterogeneity of involved actors, symbiosis in the exchange of resources, co-evolution of their interactions, and self-organisation. Designing DBEs is a task demanding a well-defined DBE’s scope, roles and responsibilities of the actors, their interactions and dependencies, as well as versatile technologies and data. The study focuses on two DBEs—Marispace-X and Skippo in the maritime domain to capture the tenets of the blue economy with dataspaces. Because the design approaches to DBE are scarce due to the paradigm’s novelty, the study aims to evaluate a model-based design method, DBEmap. The evaluation results concerning practitioners’ perceived usefulness of the DBEmap and its support for integrating DBE-related perspectives and benchmarking DBE resilience are presented. Reflections on the applicability of DBEmap and its implications for organisational support, highlighting the systematic guidance and actionable outcomes observed in the two case workshops, are discussed alongside the study’s limitations and directions for future research.

The International Conference on Perspectives in Business Informatics Research (BIR) is dedicated to business informatics, business information systems and information systems engineering. It aims to support knowledge exchange between experienced and junior researchers, practitioners and industry professionals by considering both fundamental research and application-driven studies. The 24th BIR conference, hosted by Riga Technical University, focuses on the theme “Bridging knowledge, process, and systems for responsible digital transformation” It explores how integrating knowledge management, process modeling, and information systems—with AI and smart technologies—can drive innovation and efficiency while ensuring ethical, sustainable, and inclusive practices. The conference traditionally attracts co-located workshops which encourage exchanging ideas and foster collaboration on topics related to business information systems.