The increasing adoption of Agile and DevOps development practices has transformed software delivery from periodic release cycles into continuous processes characterized by frequent code changes, automated integration, rapid deployment, and shortened feedback loops. These characteristics create significant challenges for conventional software testing because exhaustive manual validation is increasingly incompatible with development velocity and continuously changing application behavior. Artificial intelligence (AI) provides an opportunity to strengthen software testing through automated pattern recognition, classification, prediction, prioritization, and adaptive decision-making. This research and review paper examines the conceptual design of AI-enabled software testing frameworks for Agile and DevOps environments. Because the supplied literature primarily addresses AI and deep-learning techniques for automated detection and classification in signal-processing and medical domains, the studies are critically interpreted as methodological foundations rather than as direct software-testing evaluations. Their evidence demonstrates how convolutional neural networks, deep learning, automated prediction, and classification can transform complex data into actionable detection decisions (Abdelhameed et al., 2018; Acharya et al., 2018; Ahmedt-Aristizabal et al., 2018). The framework proposed in this paper integrates AI-based test generation, test prioritization, defect-risk prediction, continuous feedback, and adaptive regression testing into the Agile-DevOps pipeline. The analysis indicates that AI can potentially reduce redundant testing, improve the prioritization of high-risk test cases, and support continuous quality assessment. However, model explainability, training-data quality, false positives, integration complexity, and insufficient domain-specific evidence remain important limitations. Ramamurthy (2023) further supports the positioning of AI-driven automation as a mechanism for modern software quality engineering. The paper concludes that AI should be implemented as an adaptive decision-support layer within—not as a replacement for—the broader software testing lifecycle.

Artificial Intelligence, Software Testing, Agile Development, DevOps

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