Jul 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 289-296· 0 citations
TL;DR
The proposed framework integrates AI-assisted test case optimization with automated web testing and 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.
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
Agile software development emphasizes rapid iteration, continuous integration, frequent releases, and incremental delivery, making regression testing a central software quality challenge. Conventional regression testing approaches often depend on manually selected test suites, static prioritization rules, and repeated execution of tests that provide limited incremental fault-detection value. This paper develops a conceptual intelligent regression testing framework that applies artificial intelligence (AI) techniques to test selection, prioritization, execution, failure classification, and continuous learning within Agile development pipelines. The methodological foundation combines supervised learning, representation learning, historical test-result analysis, change-impact assessment, and feedback-driven optimization. Because the supplied literature primarily concerns AI-based detection and classification in biomedical signal-processing applications rather than software testing, the paper explicitly treats these studies as methodological evidence for transferable AI patterns rather than direct empirical evidence for regression testing. The framework consequently emphasizes feature extraction, automated classification, adaptive prediction, and real-time decision support. A conceptual evaluation indicates that AI-assisted regression testing can improve the alignment between code changes and test execution priorities, reduce redundant execution, and create feedback loops capable of adapting to changing Agile projects. However, model drift, insufficient historical data, explainability, false prioritization, and integration complexity remain significant constraints. The analysis positions intelligent regression testing as an adaptive decision-support layer rather than a complete replacement for conventional testing practices.
Tomas Kazlauskas· The American Journal of Engi...· 0 citations
This research examines an AI-enabled approach to test-case generation and optimization for modern software development by synthesizing evidence from studies concerning augmented reality, simulation-based learning, computational visualization, embedded-system monitoring, and AI-driven software quality engineering.
Dinesh Perera, Nethmi Fernando· International Journal of Nex...· 0 citations
The increasing complexity, scale, and dynamic behavior of contemporary software systems have created substantial challenges for conventional test automation approaches. Traditional automation frameworks generally depend on predefined scripts, deterministic rules, and manually maintained test artifacts, which limits their adaptability when applications, interfaces, requirements, and execution environments change continuously. This research and review paper examines the conceptual foundations of Artificial Intelligence (AI)-based test automation frameworks for next-generation software quality engineering by integrating perspectives from human-like computing, functionalism, semantic representation, conceptual spaces, distributed intelligence, and machine-intelligence measurement. The study develops a conceptual framework in which AI-supported test automation is organized around five interconnected capabilities: intelligent requirement interpretation, semantic test modeling, adaptive test generation, autonomous execution and maintenance, and predictive quality-risk analysis. The theoretical synthesis indicates that human-like and symbolic approaches can provide interpretability and structured reasoning, whereas conceptual-space and semantic approaches can support contextual representation of software behavior. Distributed intelligence perspectives further suggest opportunities for scalable quality engineering across heterogeneous testing environments. The analysis also incorporates AI-driven project-risk prediction as a complementary decision-support mechanism for prioritizing testing resources and identifying high-risk software components. Findings indicate that the most promising next-generation architecture is not a completely autonomous testing system but a human-centered, adaptive framework combining machine intelligence with explainable representations and continuous feedback. The paper identifies important limitations involving semantic ambiguity, model reliability, maintenance complexity, and the absence of universal evaluation criteria. It concludes that AI-based test automation can substantially strengthen software quality engineering when intelligence is integrated as an adaptive reasoning layer rather than treated merely as an alternative mechanism for script generation.
Priyanka Sharma· American Journal Of Applied...· 0 citations
The analysis indicates that combining predictive defect-risk scores with automated test selection can potentially reduce redundant testing, concentrate computational resources on high-risk software components, and improve feedback speed, but model reliability depends on historical defect data, feature quality, distributional stability, explainability, and integration with existing development pipelines.
Haruto Tanaka, Yuki Nakamura· Frontiers in Emerging Multid...· 0 citations
The rapid growth of web-based applications and cloud computing has significantly increased organizations' exposure to sophisticated cyber threats. Conventional penetration testing methods often require extensive manual effort, specialized expertise, and considerable time to identify security vulnerabilities, making them less suitable for continuously evolving digital infrastructures. Recent advancements in artificial intelligence (AI) and intelligent automation provide an opportunity to enhance vulnerability assessment by improving detection accuracy, reducing human intervention, and accelerating security analysis. This paper presents an AI-driven integrated framework for automated web application penetration testing that combines multiple industry-recognized security tools, including OWASP ZAP, Burp Suite Professional, FOCA, and Vega, within a unified graphical interface developed using Python. The proposed framework automates reconnaissance, vulnerability scanning, analysis, report generation, and remediation recommendation while maintaining flexibility for security professionals to customize scanning parameters. Multithreading and multiprocessing techniques are employed to improve scanning efficiency and enable the simultaneous execution of multiple security assessments. The framework also incorporates intelligent vulnerability prioritization based on severity levels and integrates structured reporting mechanisms to support organizational compliance and risk management activities. A comprehensive literature review and gap analysis identify the limitations of existing automated penetration testing solutions and motivate the development of the proposed architecture. Experimental analysis demonstrates the effectiveness of integrating multiple vulnerability scanners to improve the detection of common web application vulnerabilities such as SQL injection, cross-site scripting, broken authentication, cross-site request forgery, XML external entity attacks, insecure configurations, and API-related security flaws. Comparative analysis reveals that the integrated framework provides broader vulnerability coverage, improved usability, enhanced reporting capabilities, and greater operational efficiency than standalone scanning tools do. The proposed framework contributes to intelligent cybersecurity automation by providing a scalable, extensible, and user-friendly solution capable of supporting modern web application security assessments while reducing the complexity and cost associated with traditional penetration testing methods.
V. Bidve, K. Kakade, Vinod V. Kimbahune et al.· Journal of Cybersecurity and...· 0 citations
Cloud native applications are built and released through continuous integration and continuous delivery pipelines that call for fast feedback and frequent deployment. Traditional test automation, which depends on scripts that are written once and maintained by hand, was never really designed for this pace of change. When a microservice is redeployed, refactored, or scaled several times a day, the scripts that were written to test it tend to fall behind, and quality assurance teams end up spending more time repairing tests than writing new ones. This paper looks at how artificial intelligence can be used to build a test automation framework that adjusts itself as the application changes, rather than breaking every time something moves. We describe a framework, referred to here as AutoQA CN, that brings together three capabilities: automatic generation of new test cases from API specifications and usage data, self-healing of broken test scripts through similarity-based and learned matching, and risk-based prioritisation of test execution using a reinforcement learning agent. The framework is built to sit alongside existing continuous integration tooling in a containerised environment rather than replace it outright. We evaluate the approach through an illustrative case study across three representative microservice scenarios and compare it with a conventional scripted automation baseline on three measures: maintenance effort, time to detect faults, and pipeline throughput. The results point to meaningful reductions in maintenance work and faster fault detection, along with a modest gain in throughput. We close by discussing where these gains are likely to hold up in practice, where the approach still needs human oversight, and what would need to happen for a framework like this to be trusted in production settings.
Ihor Diakonov· Journal of Artificial Intell...· 0 citations
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