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@article{206821,
        author = {Pawan S and Swarna H R},
        title = {An Automata-Driven Machine Learning Framework for Intelligent Web Application Testing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {581-588},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206821},
        abstract = {The evolution of modern web applications towards being more dynamic and state-driven makes many traditional testing methods less efficient due to complexity of such applications. Traditional approaches to web testing often prove inefficient when dealing with multi-staged workflow, state change management, and various input-dependent application actions. This paper presents an adaptive approach to testing based on automata modeling and machine learning to facilitate adaptive testing of modern web applications. Firstly, this approach requires constructing an automata model describing possible web application navigation and interactions. On the basis of this model, systematic paths covering all available application states are derived. Machine learning is applied in order to identify the most problematic paths and optimize their selection for testing purposes. Also, the input generation process involves fuzzing techniques which allow producing unexpected and varied input values that increase the chance to detect potential vulnerabilities. The combination of automata modeling, machine learning, and fuzzing allows performing adaptive web application testing and ensures structural consistency. The presented approach provides better test coverage, fault detection ability, and decreased testing efforts in comparison with traditional methods.},
        keywords = {Automata-Based Testing, Machine Learning, Web Application Testing, Hybrid Testing Framework, Fuzzing, Intelligent Testing.},
        month = {July},
        }