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The success of software projects depends on well-defined requirements. However, specifying these requirements is a time-intensive task that requires domain expertise. Poorly defined requirements often lead to project failures, cost overruns, and inefficiencies. Large language models (LLMs) have the potential to address these challenges by assisting in requirements engineering (RE). This paper explores the capabilities of LLMs in RE, focusing on two key aspects: (i) generating coherent and contextually relevant software requirements for a given project scope and (ii) assessing their applicability in real-world projects. By comparing LLM-generated requirements with real-world requirement samples, we evaluate their relevance and utility. Our findings suggest that LLMs can enhance the efficiency of requirements engineering by generating relevant and useful requirements.

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Evaluating Large Language Models for the Automated Generation of Software Requirements

  • 会议论文

  • 发表日期：

    2026年

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  • Thomas Puchleitner,
  • Sebastian Lubos,
  • Alexander Felfernig,
  • Damian Garber

摘要

The success of software projects depends on well-defined requirements. However, specifying these requirements is a time-intensive task that requires domain expertise. Poorly defined requirements often lead to project failures, cost overruns, and inefficiencies. Large language models (LLMs) have the potential to address these challenges by assisting in requirements engineering (RE). This paper explores the capabilities of LLMs in RE, focusing on two key aspects: (i) generating coherent and contextually relevant software requirements for a given project scope and (ii) assessing their applicability in real-world projects. By comparing LLM-generated requirements with real-world requirement samples, we evaluate their relevance and utility. Our findings suggest that LLMs can enhance the efficiency of requirements engineering by generating relevant and useful requirements.