Personas are widely used to represent users' motivations, goals, and needs, yet the perspectives of underrepresented groups are rarely addressed in human-centered design. Large Language Models (LLMs) promise to generate personas from vast datasets, but achieving genuine diversity and accurate representation requires deliberate prompting. In this work, we investigate two strategies, namely system prompts and guidelines, to support users in crafting diverse personas and evaluate their effects in a controlled user study $(\mathbf{N}=\mathbf{6 6})$. Our results show that both strategies help users generate more diverse personas, with their combination achieving the highest coverage of diversity aspects. Consequently, this work aims to go beyond evaluating LLMs as tools for persona creation. We show and discuss strategies to support requirements engineers during their interaction with the LLM.
C. Lazik, Charlotte Kauter, Isabella Graßl et al.· IEEE International Requireme...· 0 citations
Debugging is an important activity in software development, yet providing actionable and comprehensible explanations for program failures remains challenging. Automated tools such as ALHAZEN and AVICENNA address this by using distinct strategies: ALHAZEN employs binary decision trees to show failure-inducing conditions, while AVICENNA uses a specification language to model complex input dependencies. To assess their impact on usability and user efficiency, we conducted a controlled within-between-subjects user study with 18 participants tasked with resolving four software bugs using either tool or no support. Quantitative results showed that both tools improved debugging efficiency compared to manual methods, with AVICENNA offering more precise diagnostics but requiring higher cognitive effort. Qualitative feedback revealed a preference for AVICENNA’s expressiveness despite its complexity. Our findings show that effective debugging tools have tradeoffs between accuracy and interpretability to support developers’ decision-making in increasingly complex software environments.
C. Lazik, M. Eberlein, Aaron Ziglowski et al.· Message Understanding Confer...· 0 citations
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