AI驱动软件质量的蓝图：整合大语言模型与现有标准
A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards
AI总结：
本文探讨了利用大语言模型提升软件质量保证的过程，结合现有标准，分析AI在需求验证、缺陷检测等任务中的应用，并提出未来发展方向。
摘要：
软件质量保证（SQA）对于交付可靠、安全和高效的软件产品至关重要。SQA过程旨在确保工作产品和过程符合预定义的条款和计划。近年来，大语言模型（LLMs）的进步为增强现有SQA过程提供了新机会，通过自动化需求分析、代码审查、测试生成和合规检查等任务。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提供了确保稳健质量实践的结构化框架。本文调查了基于LLM的SQA方法与这些公认标准的交集，强调了AI驱动解决方案如何增强传统方法，同时保持合规性和过程成熟度。我们首先回顾了基础软件质量标准和软件工程中LLM的技术基础。接下来，我们探讨了各种基于LLM的SQA应用，包括需求验证、缺陷检测、测试生成和文档维护。然后，我们将这些应用映射到关键软件质量框架，说明LLM如何在每个标准中解决特定要求和指标。经验案例研究和开源倡议展示了这些方法的实用性。同时，关于挑战（如数据隐私、模型偏差、可解释性）的讨论突显了需要有意识的治理和审计。最后，我们提出了未来方向，包括自适应学习、隐私导向的部署、多模态分析和为AI驱动软件质量演变的标准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
