With the advent of generative LLMs and their advanced code generation capabilities, some people already envision the end of traditional software engineering, as LLMs may be able to produce high-quality code based solely on the requirements a domain expert feeds into the system. The feasibility of this vision can be assessed by understanding how developers currently incorporate requirements when using LLMs for code generation—a topic that remains largely unexplored. We interviewed 18 practitioners from 14 companies to understand how they (re)use information from requirements and other design artifacts to feed LLMs when generating code. Based on our findings, we propose a theory that explains the processes developers employ and the artifacts they rely on. Our theory suggests that requirements, as typically documented, are too abstract for direct input into LLMs. Instead, they must first be manually decomposed into programming tasks, which are then enriched with design decisions and architectural constraints before being used in prompts. Our study highlights that fundamental RE work is still necessary when LLMs are used to generate code. Our theory is important for contextualizing scientific approaches to automating requirements-centric SE tasks.

Requirements engineering (RE) for digital ecosystems, i.e., systems with contributions by different players on a common technical platform, is a challenging task. The complexity of the ecosystems and the heterogeneity of the players imply the need to consider a multitude of dimensions when eliciting and analyzing requirements for the platform. This applies to the creation of new digital ecosystems, where players with services and data are on boarded on newly established platforms, as well as to the evolution of existing ecosystems. Since these RE-relevant dimensions have not been investigated systematically yet, we performed an interview series with practitioners and scientists working on actual digital ecosystems. We collected a set of 61 information needs, i.e., aspects that are relevant when making decisions on requirements. This set served as the foundation for the construction of a digital ecosystem meta-model. In this meta-model, we structured the identified dimensions such that in future work we can derive a methodology for the performance of requirements elicitation and analysis for digital ecosystems.

Our requirements engineering (RE) community has known for decades that the success or failure of RE methods heavily depends on the context in which they are applied. Thus, many experiences have been gained and shared in the community that reflect which RE methods are suitable for a specific context, such as embedded systems development (e.g., automotive or military domain) or information systems development (e.g., banking or flight control domain). Nowadays, in times of smart cities and their counterpart smart rural areas, where newly introduced IT systems have a strong effect on our society, a new and challenging context arises for RE, which opens up new research questions. As a contribution to this situation and to foster discussions in our community about whether our RE methods are appropriate in this new "social context", this perspective paper reflects on the state of the art and on our own experiences in applying RE in the context of smart rural areas. These results might also pertain in the context of smart cities that pose similar challenges to RE. In addition, we present a framework comprising both an initial classification of social contexts, particularly their end users, and a classification for RE methods. Example usage scenarios illustrate how this framework helps to reflect on the suitability of our RE methods, and, if necessary, provides the basis for adapting them or creating new ones. Finally, we outline a roadmap with research questions and related activities with which we want to encourage our community to perform the proposed research activities in order to enrich our body of experiences and adapt our methods to this highly relevant context.

In practice, there is the trend towards the development of increasingly complex software ecosystems consisting of information as well as embedded systems. The development and evolution of such ecosystems is challenging because of the involvement of a multitude of organizations providing software or hardware components on the ecosystem's platform. Therefore, for the purpose of deriving requirements towards the future development of the ecosystem and its platform, the gaining of insights into the eco-system is crucial as well as difficult. In my research, I work on the creation of an understanding of information needs in the context of smart ecosystems to build dedicated representations that support the ecosystem evolution. In this paper, I present results of an initial consideration of related work and give an outline of my planned future research including expected contributions, along with a summary of the status of the work done so far.

We are now living in the era of digital transformation: Innovative and digital business models are transforming the global business world and society. However, the authors of this paper have perceived barriers that prevent requirements engineers from contributing properly to the development of the software systems that underpin the digital transformation. We also realized that breaking down each of these barriers would contribute to requirements engineering (RE) becoming ubiquitous in certain dimensions: RE everywhere, with everyone, for everything, automated, accepting openness, and cross-domain. In this paper, we analyze each dimension of ubiquity in the scope of the interaction between requirements engineers and end users. In particular, we point out the transformation that is required to break down each barrier, present the perspective of the scientific community and our own practical perspective, and discuss our vision on how to achieve this dimension of ubiquity. Our goal is to raise the interest of the research community in providing approaches to address the barriers and move towards ubiquitous RE.

Classical spatial autoregressive models share the same weakness as the classical linear regression models, namely it is not possible to estimate non-linear relationships between the dependent and independent variables. In the case of classical linear regression a semi-parametric approach can be used to address this issue. Therefore an advanced semi-parametric modelling approach for spatial autoregressive models is introduced. Advanced semi-parametric modelling requires determining the best configuration of independent variable vectors, number of spline-knots and their positions. To solve this combinatorial optimization problem an asynchronous multi-agent system based on genetic-algorithms is utilized. Three teams of agents work each on a subset of the problem and cooperate through sharing their most optimal solutions. Through this system more complex relationships between the dependent and independent variables can be derived. These could be better suited for the possibly non-linear real-world problems faced by applied spatial econometricians.