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International Journal of Advance Scientific Research SCIENTIFIC IDEAS. INTERNATIONAL PERSPECTIVES.
ISSN 2750-1396 12 issues per year · Monthly
Crossref DOI: 10.37547/ijasr
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Articles Published:

AI-Powered Software Testing Frameworks for Enhancing Quality, Reliability, and Development Efficiency

Vol. 6 No. 08 (2026): Volume 06 Issue 08 Pages 139-149
Authors
  • Nguyen Minh Anh
    Department of Artificial Intelligence, Vietnam Institute of Digital Technology, Hanoi, Vietnam
  • Tran Quang Huy
    Department of Computer Science and Intelligent Systems, Advanced Technology University, Ho Chi Minh City, Vietnam
RESEARCH OVERVIEW

Abstract

The increasing complexity of software systems has created substantial challenges for conventional testing approaches, particularly in environments characterized by frequent releases, heterogeneous software artifacts, and continuously changing requirements. AI-powered software testing provides a potential mechanism for improving test analysis, automation, defect identification, and development efficiency by incorporating machine learning, deep learning, reinforcement learning, and adaptive decision-making techniques into testing workflows. This research and review paper develops a conceptual framework for AI-powered software testing by synthesizing the supplied literature on deep learning, segmentation, recognition, clustering, attention mechanisms, and AI-driven test automation. Although much of the reviewed literature originates from document-image analysis rather than software testing, its methodological contributions are relevant to the broader design of intelligent testing pipelines because they demonstrate how complex, noisy, and structured information can be automatically segmented, classified, and interpreted. The analysis positions AI-powered testing as a multi-layer framework consisting of test-input processing, intelligent test generation, execution and observation, defect-oriented analysis, adaptive prioritization, and continuous feedback. The findings indicate that deep learning can support complex pattern recognition, while reinforcement learning and attention-based mechanisms can potentially improve adaptive test selection and resource allocation. However, limitations involving data dependency, explainability, generalization, computational cost, and integration complexity remain significant. The paper concludes that effective AI-powered testing should be designed as a human-supervised, feedback-driven framework rather than as a completely autonomous replacement for software quality engineering.

Keywords

  • Artificial Intelligence
  • Software Testing
  • Test Automation
  • Machine Learning
SOURCES & CITATIONS

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