2026/03/17 by Shalini Chakraborty, Jan-Philipp Steghöfer
Computer Science · #cs.SE
AI coding assistants are fundamentally reshaping software development by shifting developers' effort from writing code toward specifying intent through natural language prompts. In emerging chat-based development practices such as vibe coding, prompts mediate the transformation of human intent into executable software. While Requirements Engineering (RE) emphasizes capturing, validating, and evolving requirements, current prompting practices remain informal and ad hoc. In this vision paper, we argue that prompts represent lightweight, evolving requirements artifacts that combine expressions of user needs with varying degrees of solution guidance. We use an existing conceptual model that decomposes prompts into three interrelated dimensions: Functionality and Quality (capturing intended system requirements), General Solutions (capturing architectural strategies and technology choices), and Specific Solutions (capturing implementation-level constraints and directives). Building on this conceptualization, we formulate four research hypotheses concerning (i) the evolution of prompts over time, (ii) the influence of user characteristics on prompt evolution, (iii) the relationship between prompt content and requirements validation and verification activities, and (iv) the impact of prompt characteristics on requirements and resulting software quality. We envision an empirical research agenda combining real-world AI-assisted development data, corpus analysis, and controlled experimentation to investigate these hypotheses and derive evidence-based practices for requirements-aware prompt engineering. By reframing prompts through the lens of RE, we position prompting not merely as an interaction mechanism with AI systems, but as a central software engineering concern requiring systematic study.