ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation

• URL: http://arxiv.org/abs/2505.17632v1
• Date: Fri, 23 May 2025 08:45:46 GMT
• Title: ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation
• Authors: Mohammad Kasra Habib, Daniel Graziotin, Stefan Wagner,
• Abstract summary: Software engineers can engage with ReqBrain through chat-based sessions to automatically generate software requirements.<n>Top-performing model, Zephyr-7b-beta, achieved 89.30% Fl using the BERT score and a FRUGAL score of 91.20 in generating authentic and adequate requirements.
• Score: 4.475603469482274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Requirements elicitation and specification remains a labor-intensive, manual process prone to inconsistencies and gaps, presenting a significant challenge in modern software engineering. Emerging studies underscore the potential of employing large language models (LLMs) for automated requirements generation to support requirements elicitation and specification; however, it remains unclear how to implement this effectively. In this work, we introduce ReqBrain, an Al-assisted tool that employs a fine-tuned LLM to generate authentic and adequate software requirements. Software engineers can engage with ReqBrain through chat-based sessions to automatically generate software requirements and categorize them by type. We curated a high-quality dataset of ISO 29148-compliant requirements and fine-tuned five 7B-parameter LLMs to determine the most effective base model for ReqBrain. The top-performing model, Zephyr-7b-beta, achieved 89.30\% Fl using the BERT score and a FRUGAL score of 91.20 in generating authentic and adequate requirements. Human evaluations further confirmed ReqBrain's effectiveness in generating requirements. Our findings suggest that generative Al, when fine-tuned, has the potential to improve requirements elicitation and specification, paving the way for future extensions into areas such as defect identification, test case generation, and agile user story creation.

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