Software Quality Assurance (SQA) is critical for delivering reliable, secure, and efficient software products. The Software Quality Assurance Process aims to provide assurance that work products and processes comply with predefined provisions and plans. Recent advancements in Large Language Models (LLMs) present new opportunities to enhance existing SQA processes by automating tasks like requirement analysis, code review, test generation, and compliance checks. Simultaneously, established standards such as ISO/IEC 12207, ISO/IEC 25010, ISO/IEC 5055, ISO 9001/ISO/IEC 90003, CMMI, and TMM provide structured frameworks for ensuring robust quality practices. This paper surveys the intersection of LLM-based SQA methods and these recognized standards, highlighting how AI-driven solutions can augment traditional approaches while maintaining compliance and process maturity. We first review the foundational software quality standards and the technical fundamentals of LLMs in software engineering. Next, we explore various LLM-based SQA applications, including requirement validation, defect detection, test generation, and documentation maintenance. We then map these applications to key software quality frameworks, illustrating how LLMs can address specific requirements and metrics within each standard. Empirical case studies and open-source initiatives demonstrate the practical viability of these methods. At the same time, discussions on challenges (e.g., data privacy, model bias, explainability) underscore the need for deliberate governance and auditing. Finally, we propose future directions encompassing adaptive learning, privacy-focused deployments, multimodal analysis, and evolving standards for AI-driven software quality.

翻译：软件质量保证对于交付可靠、安全且高效的软件产品至关重要。软件质量保证过程旨在确保工作产品与流程符合预定义的规定和计划。大语言模型的最新进展为增强现有软件质量保证过程提供了新的机遇，可通过自动化需求分析、代码审查、测试生成与合规性检查等任务来实现。与此同时，已确立的标准如 ISO/IEC 12207、ISO/IEC 25010、ISO/IEC 5055、ISO 9001/ISO/IEC 90003、CMMI 和 TMM 为保障稳健的质量实践提供了结构化框架。本文综述了基于大语言模型的软件质量保证方法与这些公认标准之间的交叉领域，重点探讨了人工智能驱动的解决方案如何在保持合规性与过程成熟度的同时增强传统方法。我们首先回顾了基础的软件质量标准以及大语言模型在软件工程中的技术原理。接着，我们探讨了多种基于大语言模型的软件质量保证应用，包括需求验证、缺陷检测、测试生成和文档维护。随后，我们将这些应用映射到关键的软件质量框架中，阐明大语言模型如何满足各标准内的具体要求和度量指标。实证案例研究与开源项目证明了这些方法的实际可行性。同时，关于挑战（如数据隐私、模型偏差、可解释性）的讨论强调了审慎治理与审计的必要性。最后，我们提出了未来发展方向，涵盖自适应学习、注重隐私的部署、多模态分析以及面向人工智能驱动软件质量的演进标准。