Abstract :

Software Testing Has Become Increasingly Challenging Due To The Growing Complexity Of Applications And The Demand For Faster Development Cycles. Traditional Testing Approaches Are Labor-intensive, Time-consuming, And Often Insufficient For Ensuring Software Quality, Particularly In Mobile Internet Applications That Require Testing Across Diverse Devices And Environments. This Paper Presents A Comprehensive Analysis Of AI-powered Software Testing Automation, Exploring Its Advantages, Challenges, And Future Directions. We Propose A Novel Automated Testing Framework That Integrates Multiple AI Technologies, Including Natural Language Processing, Machine Learning, And Computer Vision, To Address The Limitations Of Traditional Testing Approaches. The Framework Encompasses Automated Test Case Generation, Execution, Defect Detection, And Reporting, With Specific Adaptations For Mobile Application Testing. Case Studies Reveal Significant Improvements In Testing Efficiency (73% Reduction In Test Case Creation Time), Quality (37% Increase In Functional Coverage), And Costeffectiveness (47% Overall Cost Reduction). Our Research Demonstrates That AI-powered Testing Automation Not Only Enhances Testing Efficiency But Also Improves Defect Detection Capabilities And Enables More Comprehensive Coverage Of Testing Matrices. Despite Implementation Challenges Related To Training Data Quality, Environment Consistency, And Organizational Adoption, The Proposed Framework Offers A Promising Direction For Advancing Software Testing Practices In The Age Of AI. Keywords Artificial Intelligence, Software Testing Automation, Machine Learning, Natural Language Processing, Mobile Application Testing, Test Case Generation, Defect Detection, Quality Assurance, DevOps, Continuous Integration

Published:

10-2-2025

Issue:

Vol. 25 No. 2 (2025)

Page Nos:

55-69

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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