Elicitation interviews are the most common requirements elicitation technique, and proficiency in conducting these interviews is crucial for requirements elicitation. Traditional training methods, typically limited to textbook learning, may not sufficiently address the practical complexities of interviewing techniques. Practical training with various interview scenarios is important for understanding how to apply theoretical knowledge in real-world contexts. However, there is a shortage of educational interview material, as creating interview scripts requires both technical expertise and creativity. To address this issue, we develop a specialized GPT agent for auto-generating interview scripts. The GPT agent is equipped with a dedicated knowledge base tailored to the guidelines and best practices of requirements elicitation interview procedures. We employ a prompt chaining approach to mitigate the output length constraint of GPT to be able to generate thorough and detailed interview scripts. This involves dividing the interview into sections and crafting distinct prompts for each, allowing for the generation of complete content for each section. The generated scripts are assessed through standard natural language generation evaluation metrics and an expert judgment study, confirming their applicability in requirements engineering training.

翻译：需求获取访谈是最常用的需求获取技术，熟练掌握此类访谈技巧对于需求获取至关重要。传统的培训方法通常局限于课本学习，可能无法充分应对访谈技巧在实际应用中的复杂性。通过多样化访谈场景进行实践训练，对于理解如何在实际情境中应用理论知识具有重要意义。然而，当前教育性访谈材料较为匮乏，因为创作访谈脚本既需要专业技术知识，又需要创造性思维。为解决这一问题，我们开发了一个专门用于自动生成访谈脚本的GPT智能体。该GPT智能体配备了专门定制的知识库，内容涵盖需求获取访谈流程的指导原则和最佳实践。我们采用提示链方法以缓解GPT的输出长度限制，从而能够生成全面而详细的访谈脚本。该方法将访谈划分为多个部分，并为每个部分设计独立的提示，从而生成每个部分的完整内容。通过标准自然语言生成评估指标和专家评判研究对生成的脚本进行评估，证实了其在需求工程培训中的适用性。