AI-Driven Intelligent Test Automation Frameworks for Advanced Software Quality Management
Keywords:
Artificial Intelligence, Intelligent Test Automation, Software Quality Management, Deep LearningAbstract
The increasing complexity, scale, and continuous delivery requirements of modern software systems have exposed significant limitations in conventional test automation. Traditional automation generally depends on predefined scripts, manually designed test cases, and deterministic execution paths, making it difficult to respond efficiently to changing requirements, emerging defects, and evolving software architectures. This research and review paper examines the conceptual foundations of AI-driven intelligent test automation frameworks for advanced software quality management by synthesizing the provided literature on technology acceptance, intelligent knowledge representation, contextual awareness, machine learning, structured learning, cybersecurity education, and technology–performance relationships. The study develops a conceptual framework in which artificial intelligence supports test knowledge acquisition, test-case generation, prioritization, execution, defect interpretation, and continuous quality feedback. Particular attention is given to the relationship between system usefulness and usability, represented through the technology acceptance perspective of Adams, Nelson, and Todd (1992), and the task-technology fit perspective of Goodhue and Thompson (1995). Transformer-based language representations provide a foundation for processing software artifacts and testing knowledge, while knowledge-graph principles support relationships among requirements, code components, test cases, defects, and quality outcomes. The review indicates that intelligent automation is most effective when AI capabilities are integrated with contextual awareness, structured knowledge, human oversight, and continuous feedback rather than deployed as isolated prediction mechanisms. The resulting framework provides a theoretically grounded approach for improving test adaptability, defect detection, prioritization, and quality-management decision making. Limitations concerning interpretability, training-data dependency, domain transfer, and human validation are also identified.
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