Multi-LLM Persona Generation for Virtual Focus Groups in Software Engineering: A Controlled, Multi-domain Study of Emotional Requirements Elicitation

Emotional requirements (ERs)---how users should feel and which emotional harms a system must avoid---often determine whether people adopt and keep using software, especially in sensitive domains. Yet, eliciting ERs via interviews, workshops, and focus groups is costly and hard to scale or repeat. We study whether simulated focus groups, moderated discussions among user personas generated and played by large language models (LLMs), can augment early ER elicitation. Across three domains (mental‑health journaling, personal finance, and fitness), we compare one‑shot and iterative single‑model pipelines with iterative pipelines that use two or three different LLMs, and we benchmark against two human baselines: a human focus group and Emotional Goal Modeling (EGM). Multi‑LLM pipelines generate more diverse personas and increase the share of AI‑only ERs (not seen in our human baselines) that source‑blinded raters judge relevant by 14.7 percentage points compared to an iterative single‑model workflow; same‑model controls suggest this gain is not solely due to provider differences. Compared to human methods, simulations contribute more innovative requirements, EGM contributes clearer and more feasible ones, and human focus groups provide more natural phrasing. Overall, the results support an augmentation‑oriented workflow: use multi‑LLM simulation to broaden candidate ERs, structured modeling to organize them, and human engagement to ground decisions.