Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
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. By uniting insights from academic research, industry best practices, and established quality frameworks, we provide a comprehensive blueprint for integrating LLMs into SQA in a trustworthy, efficient, and standards-aligned manner. Index Terms—Software Quality Assurance (SQA), Large Language Models (LLMs), ISO/IEC 12207, ISO/IEC 25010, ISO/IEC 5055, ISO 9001, ISO/IEC 90003, TMM, AI in software engineering, code review automation, test generation, requirement validation, compliance auditing, software quality standards, process maturity....
The integration of generative artificial intelligence (AI) into software development processes represents a paradigm shift in how individuals interact with technology creation tools. This article examines the emergence of intuitive programming approaches colloquially termed "vibe coding" alongside traditional no-code and low code platforms, analyzing their combined potential to democratize software engineering practices. Through systematic analysis of current research, It identifies key technological frameworks, implementation challenges, and potential socioeconomic implications of AI-assisted development environments. The article findings suggest that generative AI fundamentally transforms the accessibility paradigm by bridging natural language expression with functional software creation, potentially reducing traditional barriers to entry while introducing new considerations regarding technical depth, sustainability, and equity in software production ecosystems....
Artificial Intelligence (AI) has been applied to various areas of software engineering, including requirements engineering, coding, testing, and debugging. This has led to the emergence of AI for Software Engineering as a distinct research area within software engineering. With the development of quantum computing, the field of Quantum AI (QAI) is arising, enhancing the performance of classical AI and holding significant potential for solving classical software engineering problems. Some initial applications of QAI in software engineering have already emerged, such as software test optimization. However, the path ahead remains open, offering ample opportunities to solve complex software engineering problems with QAI cost-effectively. To this end, this paper presents open research opportunities and challenges in QAI for software engineering that need to be addressed....
Large Language Models (LLMs) are used for many different software engineering tasks. In software architecture, they have been applied to tasks such as classification of design decisions, detection of design patterns, and generation of software architecture design from requirements. However, there is little overview on how well they work, what challenges exist, and what open problems remain. In this paper, we present a systematic literature review on the use of LLMs in software architecture. We analyze 18 research articles to answer five research questions, such as which software architecture tasks LLMs are used for, how much automation they provide, which models and techniques are used, and how these approaches are evaluated. Our findings show that while LLMs are increasingly applied to a variety of software architecture tasks and often outperform baselines, some areas, such as generating source code from architectural design, cloud-native computing and architecture, and checking conformance remain underexplored. Although current approaches mostly use simple prompting techniques, we identify a growing research interest in refining LLM-based approaches by integrating advanced techniques....
Software engineering has evolved dramatically from a discipline focused primarily on code implementation to a multifaceted profession encompassing comprehensive system design, cross-functional collaboration, and strategic decision-making. This transformation reflects the increasing complexity of technology ecosystems and the critical need for engineers who can navigate both technical depth and interdisciplinary breadth. The journey from coding specialist to versatile software professional involves mastering architectural thinking, developing adaptive expertise, and cultivating communication capabilities that bridge technical and domain-specific contexts. As systems grow increasingly interconnected, software engineers must balance innovation with stability, employing sophisticated approaches to requirements gathering, system design, implementation, testing, and maintenance. The integration of artificial intelligence into development workflows represents the latest evolutionary phase, augmenting human capabilities while raising important questions about ethical implementation and appropriate collaboration models. The most successful software professionals navigate this landscape by maintaining strong foundational knowledge while embracing continuous learning and adaptation. This comprehensive perspective positions software engineering as a dynamic journey rather than a static skillset, requiring practitioners to evolve alongside technology while preserving core engineering principles that transcend specific tools or frameworks....
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