Robin Gröpler

Hailiang Liu, Robin Gröpler, Gerald Warnecke

We design, analyze and numerically validate a novel discontinuous Galerkin method for solving the coagulation-fragmentation equations. The DG discretization is applied to the conservative form of the model, with flux terms evaluated by Gaussian quadrature with $Q=k+1$ quadrature points for polynomials of degree $k$. The positivity of the numerical solution is enforced through a simple scaling limiter based on positive cell averages. The positivity of cell averages is propagated by the time discretization provided a proper time step restriction is imposed.

Viju Sudhi, Libin Kutty, Robin Gröpler

It is a long-standing desire of industry and research to automate the software development and testing process as much as possible. In this process, requirements engineering (RE) plays a fundamental role for all other steps that build on it. Model-based design and testing methods have been developed to handle the growing complexity and variability of software systems. However, major effort is still required to create specification models from a large set of functional requirements provided in natural language. Numerous approaches based on natural language processing (NLP) have been proposed in the literature to generate requirements models using mainly syntactic properties. Recent advances in NLP show that semantic quantities can also be identified and used to provide better assistance in the requirements formalization process. In this work, we present and discuss principal ideas and state-of-the-art methodologies from the field of NLP in order to guide the readers on how to create a set of rules and methods for the semi-automated formalization of requirements according to their specific use case and needs. We discuss two different approaches in detail and highlight the iterative development of rule sets. The requirements models are represented in a human- and machine-readable format in the form of pseudocode. The presented methods are demonstrated on two industrial use cases from the automotive and railway domains. It shows that using current pre-trained NLP models requires less effort to create a set of rules and can be easily adapted to specific use cases and domains. In addition, findings and shortcomings of this research area are highlighted and an outlook on possible future developments is given.

Robin Gröpler, Steffen Klepke, Jack Johns et al.

Generative AI (GenAI) has recently emerged as a groundbreaking force in Software Engineering, capable of generating code, identifying bugs, recommending fixes, and supporting quality assurance. While its use in coding tasks shows considerable promise, applying GenAI across the entire Software Development Life Cycle (SDLC) has not yet been fully explored. Critical uncertainties in areas such as reliability, accountability, security, and data privacy demand deeper investigation and coordinated action. The GENIUS project, comprising over 30 European industrial and academic partners, aims to address these challenges by advancing AI integration across all SDLC phases. It focuses on GenAI's potential, the development of innovative tools, and emerging research challenges, actively shaping the future of software engineering. This vision paper presents a shared perspective on the future of GenAI-driven software engineering, grounded in cross-sector dialogue as well as experiences and findings within the GENIUS consortium. The paper explores four central elements: (1) a structured overview of current challenges in GenAI adoption across the SDLC; (2) a forward-looking vision outlining key technological and methodological advances expected over the next five years; (3) anticipated shifts in the roles and required skill sets of software professionals; and (4) the contribution of GENIUS in realising this transformation through practical tools and industrial validation. This paper focuses on aligning technical innovation with business relevance. It aims to inform both research agendas and industrial strategies, providing a foundation for reliable, scalable, and industry-ready GenAI solutions for software engineering teams.