# Leveraging LLMs for the Quality Assurance of Software Requirements

arXiv: https://arxiv.org/abs/2408.10886 · HTML: https://mlsanity.com/paper/2408.10886/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2024.

## Built on by (most cited first, 15 of 15)

- [Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale](https://mlsanity.com/paper/2504.09283/) (2025)
- [Can LLMs Generate User Stories and Assess Their Quality?](https://mlsanity.com/paper/2507.15157/) (2025)
- [A Case Study Investigating the Role of Generative AI in Quality Evaluations of Epics in Agile Software Development](https://mlsanity.com/paper/2505.07664/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [U2F: Encouraging SWE-Agent to Seize Novelty without Losing Feasibility](https://mlsanity.com/paper/2511.03517/) (2025)
- [OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering](https://mlsanity.com/paper/2512.15979/) (2025)
- [An Interactive Agent for Requirement-Driven Candidate Sourcing](https://mlsanity.com/paper/2608.23501/) (2026)
- [LLM assisted web application functional requirements generation: A case study of four popular LLMs over a Mess Management System](https://mlsanity.com/paper/2505.18019/) (2025)
- [Generating Privacy Stories From Software Documentation](https://mlsanity.com/paper/2506.23014/) (2025)
- [evalSmarT: An LLM-Based Framework for Evaluating Smart Contract Generated Comments](https://mlsanity.com/paper/2507.20774/) (2025)
- [The Software Engineering Simulations Lab: Agentic AI for RE Quality Simulations](https://mlsanity.com/paper/2511.17762/) (2025)
- [DeepQuali: Initial results of a study on the use of large language models for assessing the quality of user stories](https://mlsanity.com/paper/2602.08887/) (2026)
- [Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making](https://mlsanity.com/paper/2602.11924/) (2026)
- [Iterative Audit Convergence in LLM-Managed Multi-Agent Systems: A Case Study in Prompt-Engineering Quality Assurance](https://mlsanity.com/paper/2605.12280/) (2026)
- [Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering](https://mlsanity.com/paper/2608.07038/) (2026)

## Abstract (arXiv)

Successful software projects depend on the quality of software requirements. Creating high-quality requirements is a crucial step toward successful software development. Effective support in this area can significantly reduce development costs and enhance the software quality. In this paper, we introduce and assess the capabilities of a Large Language Model (LLM) to evaluate the quality characteristics of software requirements according to the ISO 29148 standard. We aim to further improve the support of stakeholders engaged in requirements engineering (RE). We show how an LLM can assess requirements, explain its decision-making process, and examine its capacity to propose improved versions of requirements. We conduct a study with software engineers to validate our approach. Our findings emphasize the potential of LLMs for improving the quality of software requirements.