Next-Generation Software Testing: Integrating Artificial Intelligence with Automated Quality Engineering

Abstract

The increasing complexity of software systems has created a need for testing approaches that are adaptive, automated, context-aware, and capable of operating continuously across the software development lifecycle. Traditional automation improves execution speed but often remains dependent on manually designed test cases, predefined rules, and static validation mechanisms. This research and review paper examines the conceptual integration of Artificial Intelligence (AI) with automated quality engineering, with particular attention to intelligent test generation, context-aware validation, automation frameworks, user-experience assessment, and technology-enabled quality assurance. The study employs a structured qualitative synthesis of the supplied literature, which includes research on augmented reality (AR), QR-code-based applications, mobile platforms, Unity3D, Vuforia, Blender, digital applications, and user-experience evaluation. Although the reviewed studies do not exclusively address AI-based software testing, they collectively demonstrate technological characteristics—automation, interaction awareness, application compatibility, platform integration, and user-centered validation—that are relevant to the design of next-generation testing environments. The analysis positions AI-driven testing as an evolution from script-centric automation toward adaptive quality engineering in which test selection, execution, validation, and feedback can be increasingly supported by intelligent mechanisms. The paper proposes a conceptual AI-enabled automated quality engineering framework comprising application understanding, test intelligence, execution orchestration, result interpretation, and continuous feedback layers. Findings indicate that the effectiveness of such frameworks depends not merely on AI adoption but on integration with application architecture, development workflows, platform constraints, and human quality expertise. The study concludes that AI can significantly strengthen automated testing when deployed as an augmentation mechanism rather than as an isolated replacement for established engineering practices.