Intelligent Web Application Automated Testing Framework with AI-Assisted Test Case Optimization

Abstract

Modern software development demands rapid delivery with continuous integration, making intelligent testing essential for maintaining quality. Traditional automation tools like Selenium and JUnit execute all test cases without prioritization, resulting in resource inefficiency and longer testing cycles. To address this, AI-based intelligent frameworks are emerging that dynamically optimize test execution by predicting critical areas and prioritizing impactful tests based on historical results. The project aims to design and implement an intelligent web application testing framework that automates functional testing while learning from past results to enhance efficiency. It employs AI-based models to prioritize and optimize test case execution, provides visual analytics on performance trends, and supports seamless integration with CI/CD tools like Jenkins and GitHub Actions. This framework offers developers and testers a scalable, adaptive, and data-driven solution for smarter and faster software testing. Existing frameworks like Selenium, JUnit, and TestNG offer robust automation but lack intelligent test prioritization. Key limitations include redundant execution of stable tests, extended testing time, high maintenance overhead, absence of AI-driven optimization, and limited analytical insights. These gaps highlight the need for AI-powered automation solutions that enhance efficiency, reduce redundancy, and support data-driven test management. The proposed framework integrates AI-assisted test case optimization with automated web testing. Key features include automated Selenium-based test execution, AI-driven prioritization using historical data, dynamic adjustment of test order, a result visualization dashboard, and seamless CI/CD integration. This approach reduces redundant tests, enhances coverage, and accelerates feedback, enabling faster and more reliable software releases