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Author

N. N. Kumar

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Open access Jul 2026

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

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

Amireddy Sainath Reddy, N. N. Kumar · 0 citations
Open access Jul 2026

A Django-Enabled Hybrid Framework for Intelligent Face Morph Synthesis and Authentication Resilience

Facial recognition systems are widely used for identity verification but are vulnerable to face morphing attacks, where multiple facial images are blended to form a deceptive identity that can fool recognition models. This project develops a deep learning-based approach to detect such attacks and strengthen biometric authentication systems. It lies in the domain of Artificial Intelligence and Machine Learning, focusing on Computer Vision techniques to differentiate real and morphed facial images for accurate and secure verification. The project involves creating realistic morphed face datasets and building an efficient detection model applicable to border control, ID verification, and digital authentication. Current systems fail against high-quality morphs produced using advanced tools, showing reduced accuracy under variations in lighting, age, and facial accessories. To overcome this, the proposed model combines deep learning-based feature extraction with machine learning classifiers. Morph-2 and Morph-3 datasets are generated using professional morphing tools, and image enhancement with feature fusion is applied to improve accuracy and robustness.

Mekala Pooja, N. N. Kumar · 0 citations