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GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training

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arxiv 2406.11439 v1 pith:4CERPP5Y submitted 2024-06-17 cs.SE cs.AI

classification cs.SEcs.AI
keywords interviewelicitationrequirementsscriptstrainingaddressagentengineering
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Requirements Elicitation Follow-Up Question Generation

    cs.SE 2025-07 conditional novelty 6.0 of 10

    GPT-4o-generated follow-up questions are rated equal to human questions when minimally guided, and better when guided by a 14-type interviewer mistake framework.

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