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Artificial intelligence, particularly large language models, supports various disciplines, including software engineering. This article presents a case study to analyze the evolution and feasibility of using LLMs in the software engineering process, employing various tools for generating interviews during the elicitation phase and for requirement analysis and specification through user stories. Furthermore, the study highlights significant evolutionary improvements in model reasoning and modular segmentation, leading to more structured and nuanced outputs. By comparing experimental phases, the case study demonstrates how these advancements enhance the overall applicability and effectiveness of LLMs in complex software development tasks.

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Tracking the Evolution of Large Language Models in Requirements Elicitation and Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Ailén Panigo,
  • Kristian Petkoff Bankoff,
  • Ariel Pasini,
  • Patricia Pesado

摘要

Artificial intelligence, particularly large language models, supports various disciplines, including software engineering. This article presents a case study to analyze the evolution and feasibility of using LLMs in the software engineering process, employing various tools for generating interviews during the elicitation phase and for requirement analysis and specification through user stories. Furthermore, the study highlights significant evolutionary improvements in model reasoning and modular segmentation, leading to more structured and nuanced outputs. By comparing experimental phases, the case study demonstrates how these advancements enhance the overall applicability and effectiveness of LLMs in complex software development tasks.