Jan-Philipp Steghöfer

Shalini Chakraborty, Jan-Philipp Steghöfer

AI coding assistants are reshaping software development by shifting focus from writing code to formulating prompts. In chat-focused approaches such as vibe coding, prompts become the primary arbiter between human intent and executable software. While Requirements Engineering (RE) emphasizes capturing, validating, and evolving requirements, current prompting practices remain informal and adhoc. We argue that prompts should be understood as lightweight, evolving requirement artifacts that blend requirements with solution guidance. We propose a conceptual model decomposing prompts into three interrelated components: Functionality and Quality (the requirement), General Solutions (architectural strategy and technology choices) and Specific Solutions (implementation-level constraints). We assess this model using existing prompts, examining how these components manifest in practice. Based on this model and the initial assessment, we formulate four hypotheses: prompts evolve toward specificity, evolution varies by user characteristics, engineers using prompting engage in increased requirement validation and verification, and progressive prompt refinement yields higher code quality. Our vision is to empirically evaluate these hypotheses through analysis of real-world AI-assisted development, with datasets, corpus analysis, and controlled experiments, ultimately deriving best practices for requirements-aware prompt engineering. By rethinking prompts through the lens of RE, we position prompting not merely as a technical skill, but as a central concern for software engineering's future.

Anton Hummel, Håkan Burden, Susanne Stenberg et al.

The EU AI Act makes explainability urgent for high-risk AI systems, yet most XAI research focuses on technical metrics rather than regulatory compliance. Understanding how legal requirements reshape XAI method design is challenging: the AI Act regulates organizational relationships (providers, deployers) using legal terminology, specifies obligations without concrete technical requirements, and underrepresents end-users--the very stakeholders whose needs human-centered XAI addresses. As regulations emerge globally, human-centered XAI practitioners face both a challenge and an opportunity: regulations pull XAI research toward real-world deployment, while practitioners can actively shape how explainability enables compliance. This establishes a bidirectional relationship. Our contribution is threefold. First, we provide the first interdisciplinary analysis of XAI's role in the AI Act--conducted by a team comprising AI Act legal experts, ML engineers, and requirements engineers--on a real-world clinical decision support system. Second, we systematically align XAI stakeholder roles with AI Act legal responsibilities, revealing where explainability methods address regulatory requirements versus where additional measures are necessary. Third, we identify three key opportunities for human-centered XAI practitioners: actively defining their roles in regulatory implementation; making the user-to-affected-party relationship explicit where regulations address only provider-deployer obligations; and enabling compliance while building multi-level trust--from regulators to affected parties.