AI-Driven Continuous Testing Frameworks for Agile Software Quality Engineering

Abstract

Agile software development requires rapid delivery, frequent change, continuous integration, and sustained product quality. Conventional testing approaches often struggle to maintain adequate coverage and timely feedback when software changes occur at high frequency. AI-driven continuous testing provides a potential framework for addressing this challenge by combining automated test selection, adaptive execution, defect-oriented prioritization, and feedback-driven quality assessment within the software delivery lifecycle. This research-review paper develops a conceptual framework for AI-driven continuous testing by synthesizing the functional principles represented in the provided literature, particularly research concerning locomotion planning, coordination, adaptive control, and biomimetic systems. Although the supplied references primarily concern robotic systems rather than software testing, their treatment of coordinated movement, path recognition, gait planning, kinematic modeling, and control provides transferable theoretical perspectives for designing adaptive testing processes. The paper proposes a layered continuous testing framework consisting of change analysis, intelligent test prioritization, adaptive execution, quality assessment, and continuous feedback. The analysis indicates that AI-driven testing can be theoretically positioned as a closed-loop control process in which test activities respond dynamically to software changes and observed quality signals. The study further identifies challenges involving explainability, training requirements, false positives, integration complexity, and the limitations of transferring concepts from robotics to software quality engineering. The resulting framework offers a conceptual foundation for integrating intelligent decision-making with Agile testing while emphasizing the need for empirical validation using real software-development datasets.