Context: Digital twins (DTs) have become integral in diverse cyber–physical production systems (CPPS), enabling dynamic interactions between physical entities and their digital counterparts. Yet their integration into such complex ecosystems raises substantial interoperability challenges. While these challenges and associated frameworks for DTs have been extensively theorized in scholarly literature, there are limited empirical investigations that capture industrial perspectives on these aspects. Objective: This exploratory study aims to empirically investigate real-world interoperability challenges in DT deployments and assess the relevance of a layered interoperability framework as a structured approach to address these issues. Methods: We addressed this gap by conducting interviews with 12 DT practitioners from 10 companies across five European countries. Interviewees are guided through two reference models: a simplified view of the DT ecosystem and a layered framework based on the Level of Conceptual Interoperability Model (LCIM). The thematic synthesis and systematic mapping of the collected data have used Grounded Theory (GT)-based open coding. Sentiment analysis was used as an illustrative complement to the qualitative findings by capturing expert attitudes towards the LCIM for DTs. Results: The analysis identified 26 practical interoperability challenges, thematically synthesized into 7 categories. Experts’ perspectives on the LCIM for DTs revealed two key outcomes: 4 drivers of the open and closed-ended nature of interoperability layers, and 4 value propositions highlighting the framework’s relevance for DT deployments. Further, the identified challenge categories are mapped across layers, highlighting the dichotomy of open-source and proprietary approaches, the need for Dynamism and Ecosystem-oriented Interoperability. Conclusions: This work advances empirical and theoretical understandings of DT interoperability within CPPS. Our findings contribute to addressing practical interoperability challenges, provide empirical values for the layered model in cross-disciplinary approaches to DT integration, and offer guidance for researchers and practitioners. Future work could validate and adapt the layered approach through domain-specific DT applications to assess its effectiveness in digital transformation initiatives.

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students’ learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e. individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students’ comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

NoSQL databases are essential for managing modern data-intensive applications. While SQL education is a crucial part of the software engineering and computer science curriculum, it is insufficient in addressing the rise of big data and cloud infrastructures. Despite extensive research on SQL education, there is limited exploration of NoSQL education, particularly in teaching methods and data models. This study addresses this gap by conducting a systematic literature review on NoSQL database education, aiming to assess current research, teaching practices, models, tools, scalability, and security mechanisms while offering a framework for integrating NoSQL into academic curricula. Out of 386 articles, 28 were selected for detailed analysis, focusing on NoSQL teaching methods, models, and curriculum development. Findings revealed that document-oriented and graph databases , especially MongoDB , Cassandra, and Neo4j , are the most taught. The project-based learning approach was the most common teaching method. Challenges identified include adapting to technological advancements, addressing diverse student needs, and the shift to online learning. This review contributes valuable insights into NoSQL education and offers recommendations for improving teaching practices in software engineering curricula.

Digital twin (DT) ecosystems are rapidly evolving, connecting many stakeholders, such as manufacturers, customers, and application platform providers. These ecosystems require collaboration and interaction between diverse actors to create value. This study delves into the collaboration of such stakeholders within DT-focused ecosystems. This research aims to understand stakeholder collaboration within DT ecosystems, identify potential challenges, and provide insights for managing these stakeholders. It also seeks to define the DT ecosystem and its implications for both research and practice. A systematic literature review was conducted, supplemented by empirical evidence gathered from interviews with DT experts who were knowledgeable about the DT ecosystem. The study also analyzed DT systems, stakeholder roles, and the challenges with ecosystem-focused DT development. The study identified various stakeholders and their roles in adding value to a DT ecosystem. It highlighted the benefits of stakeholder collaboration, such as knowledge gain during DT system development. The research also revealed the technical and non-technical challenges encountered in ecosystem-focused DTs, emphasizing the importance of standardization as a solution. A new definition of the DT ecosystem was proposed, emphasizing its data-driven nature, interconnected DTs, stakeholder value creation, and technology enablement. Stakeholder collaboration is pivotal in DT ecosystems, with each actor playing a distinct role. Addressing challenges, especially through standardization (OPC UA and ISO 23247), can lead to more efficient and coherent DT ecosystems. The insights provided by this study can guide industries in designing, developing, and maintaining their DT ecosystems, ensuring value creation and stakeholder satisfaction. Future research avenues that emphasize the importance of understanding the challenges involved and deploy appropriate solutions were suggested.

Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context. Objective: We aim to collect data related to engineering goals, challenges, and practices in start-up companies to ascertain trends and patterns characterizing engineering work in start-ups. Such data allows researchers to understand better how goals and challenges are related to practices. This understanding can then inform future studies aimed at designing solutions addressing those goals and challenges. Besides, these trends and patterns can be useful for practitioners to make more informed decisions in their engineering practice. Method: We use a case survey method to gather first-hand, in-depth experiences from a large sample of software start-ups. We use open coding and cross-case analysis to describe and identify patterns, and corroborate the findings with statistical analysis. Results: We analyze 84 start-up cases and identify 16 goals, 9 challenges, and 16 engineering practices that are common among start-ups. We have mapped these goals, challenges, and practices to start-up life-cycle stages (inception, stabilization, growth, and maturity). Thus, creating the progression model guiding software engineering efforts in start-ups. Conclusions: We conclude that start-ups to a large extent face the same challenges and use the same practices as established companies. However, the primary software engineering challenge in start-ups is to evolve multiple process areas at once, with a little margin for serious errors.

[Context and motivation] Due to lack of resources and teams with low levels of experience, startups face several challenges during their product development, such as product customization, attracting new customers, and mastering the technology uncertainty. To increase their market presence and compensate for their lack of resources, startups need to consider other options such as joint ventures and partnerships. [Question/problem] Some companies that share highly similar resources and businesses can be potential competitors with one another. The effect of interaction with such potential competitors with respect to startups to obtain expertise has not been often reported in the literature to date. [Principal ideas/results] In this study, we simulated two software startups in a controlled experiment to evaluate the effect of interaction with a potential competitor in the effort estimation process. A real startup case was also involved in analyzing the effect. The results of the study show that there is a statistically significant difference in the effectiveness when co-operating with a competitor in the process. Experiment participants also considered the interaction with the potential competitor useful based on the exchange of important information and ideas regarding the product domain. [Contribution] This paper contributes by demonstrating the effect of interaction with a potential competitor in the effort estimation process. In addition, our study encourages further research on startups working in with the competitors in other software engineering knowledge areas.