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@article{188321,
        author = {Raj Sagar and Dr. Pushpneel Verma},
        title = {AI-Driven Software Testing: A Comprehensive Analysis of Tools and Techniques for Enhancing Software Quality Control},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1130-1140},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188321},
        abstract = {The rapid evolution of artificial intelligence (AI) has revolutionized software testing, enabling automation, efficiency, and accuracy in quality assurance processes. This paper provides a comprehensive analysis of AI-driven software testing tools and techniques, highlighting their impact on enhancing software quality control. It explores key methodologies, including machine learning-based test case generation, intelligent test automation, predictive defect detection, and self-healing testing frameworks. Furthermore, we evaluate prominent AI-powered testing tools, assessing their capabilities, strengths, and limitations. The study also discusses challenges such as model interpretability, data bias, and integration complexities in AI-driven testing. By synthesizing recent advancements, this research aims to guide software engineers, testers, and researchers toward leveraging AI for optimizing testing workflows and improving software reliability.},
        keywords = {artificial intelligence (AI), software testing, automation, machine learning, software reliability},
        month = {December},
        }