Johannes Viehmann

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.

Jürgen Großmann, Michael Felderer, Johannes Viehmann et al.

This article provides a taxonomy for risk-based testing that serves as a tool to define, tailor, or assess risk-based testing approaches in general and to instantiate risk-based testing approaches for the current testing standards ISO/IEC/IEEE 29119, ETSI EG and OWASP Security Testing Guide in particular. We demonstrate the usefulness of the taxonomy by applying it to the aforementioned standards as well as to the risk-based testing approaches SmartTesting, RACOMAT, PRISMA and risk-based test case prioritization using fuzzy expert systems. In this setting, the taxonomy is used to systematically identify deviations between the standards' requirements and the individual testing approaches so that we are able to position and compare the testing approaches and discuss their potential for practical application.