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[2505.13766] Advancing Software Quality: A Standards-Focused Review of LLM-Based Assurance Techniques
Summary
This article reviews the integration of Large Language Models (LLMs) in Software Quality Assurance (SQA), highlighting their potential to enhance traditional quality practices through automation and compliance with established standards.
Why It Matters
As software quality becomes increasingly critical in technology development, understanding how LLMs can improve SQA processes is essential. This review connects AI advancements with established quality standards, providing insights into enhancing software reliability and efficiency.
Key Takeaways
- LLMs can automate key SQA tasks like requirement analysis and code review.
- Established standards such as ISO/IEC provide frameworks for integrating LLMs into SQA.
- Empirical case studies demonstrate the practical application of LLMs in enhancing software quality.
- Challenges like data privacy and model bias must be addressed for effective LLM deployment.
- Future directions include adaptive learning and evolving standards for AI-driven quality assurance.
Computer Science > Software Engineering arXiv:2505.13766 (cs) [Submitted on 19 May 2025 (v1), last revised 15 Feb 2026 (this version, v3)] Title:Advancing Software Quality: A Standards-Focused Review of LLM-Based Assurance Techniques Authors:Avinash Patil View a PDF of the paper titled Advancing Software Quality: A Standards-Focused Review of LLM-Based Assurance Techniques, by Avinash Patil View PDF HTML (experimental) Abstract: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...
