| dc.description.abstract | The increasing complexity of modern software systems has intensified the need for
effective and efficient Software Quality Assurance (SQA) practices. Traditional SQA
approaches often face limitations such as ambiguous requirements, incomplete test
coverage, delayed defect detection, and a heavy reliance on manual testing. To address
these challenges, recent research has explored the integration of Agentic Artificial
Intelligence (AI), which is capable of autonomous decision-making, adaptive behavior,
and goal-oriented reasoning. This study presents a systematic review of existing
literature to examine how Agentic AI techniques are applied within SQA processes
across the software development lifecycle. Using a structured review methodology,
relevant peer-reviewed studies were analyzed to identify key application areas, including
automated requirements clarification, intelligent test-case generation, predictive defect
analysis, and continuous quality monitoring. The findings indicate that Agentic
AI-based approaches contribute to improved early detection, reduced human error,
enhanced testing efficiency, and increased adaptability to evolving project requirements
when compared to traditional SQA methods. However, the review also highlights several
challenges, such as model interpretability issues, data dependency, and integration
complexity, which limit widespread adoption. Overall, this review demonstrates that
Agentic AI has significant potential to enhance the effectiveness and reliability of SQA
practices, while also identifying current limitations that must be addressed to enable
broader practical implementation. | en_US |
