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Терновий І. М., Khamula O. H. № 1 (70) 83-92 Image Image

The digital transformation era has elevated software quality from a technical concern to a critical business and societal imperative. This comprehensive review synthesizes advancements in software quality assessment (SQA) methodologies from 2020–2025, addressing the convergence of evolving quality models, AI-driven automation, and or­ganizational paradigms like DevOps. The analysis reveals a paradigm shift from reactive defect detection to proactive, intelligent quality assurance (QA) embedded throughout the software lifecycle.

Traditional frameworks like ISO/IEC 9126 and its successor ISO/IEC 25010 provide structured quality attributes. CMMI remains pivotal for assessing organizational maturity in delivering high-quality software. However, specialized models now address context-specific needs—such as security-critical systems or early-stage startups—signaling a move beyond one-size-fits-all approaches. This trend underscores the necessity for domain-adaptive quality assessment, where contextual factors shape evaluation criteria.

Artificial Intelligence (AI) and Machine Learning (ML) are redefining SQA:Test Automation, Predictive Capabilities, Self-Healing Tests, Enhanced Non-Functional Tes­ting. DevOps and Shift-Left/Shift-Right testing are foundational to continuous quality.

Software quality assessment is transitioning toward intelligent, continuous, and ethical assurance. AI’s predictive power and automation capabilities redefine tester roles—shifting focus from execution to strategic oversight. DevOps and Shift-Left/Right methodologies enable holistic quality cycles. However, methodological rigor in empirical research, ethical AI deployment, and context-aware quality models remain urgent priorities. Success hinges on bridging industry-academia gaps and advancing adaptive, human-centric QA frameworks for resilient, trustworthy software.

Keywords: Software Quality Assurance, Artificial Intelligence in Testing, DevOps, Quality Models, Shift-Left Testing, Shift-Right Testing, AI Ethics, Predictive Analytics, Empirical Software Engineering, Test Automation, Continuous Quality

doi: 10.32403/1998-6912-2025-1-70-72-82


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