Journal of Information Systems Engineering and Management, 2025
The progression of software testing has evolved from manual processes to automated systems. Howev... more The progression of software testing has evolved from manual processes to automated systems. However, the emergence of Agentic AI-driven testing represents the next transformative leap. These intelligent agents autonomously generate, execute, and optimize tests, redefining the quality assurance (QA) landscape. Agentic AI-defined by its capacity to independently perceive, plan, execute, and learn-has emerged as a transformative force in software testing. This article examines the impact of Agentic AI on the software testing lifecycle, highlighting its core capabilities, such as dynamic test generation, autonomous execution, intelligent root-cause analysis, multi-modal command interpretation, and context-aware decision-making. These capabilities enable a significant shift from brittle test scripts and reactive maintenance to proactive, adaptive, and self-optimizing testing systems. We further introduce a novel architectural framework that applies Agentic AI principles to automated test scenario generation. This multi-agent system comprises a Perception Module for requirement and code understanding, a Cognitive Module for strategic planning and intelligent scenario design, and an Action Module for executing, analyzing, and learning from tests. Built on state-of-the-art technologies-including large language models (LLMs), retrieval-augmented generation (RAG), deep learning, and vector databases-our framework enables seamless integration with CI/CD pipelines, supports multi-format output generation, and incorporates continuous learning for test optimization. The proposed architecture demonstrates how Agentic AI can enhance test coverage, improve software reliability, and reduce the cost and effort of maintaining large-scale testing infrastructures. It provides an intelligent, scalable, and future-ready solution for quality assurance in fast-paced, modern development environments.
International Journal of Computer Engineering and Technology (IJCET), 2025
The evolution of modern web applications has necessitated sophisticated testing strategies that c... more The evolution of modern web applications has necessitated sophisticated testing strategies that can keep pace with rapid deployment cycles while ensuring comprehensive coverage and security. Automation testing has emerged as a critical component of the software quality assurance process, providing validation of complete user workflows and system integrations. However, the integration of automation testing into CI/CD pipelines presents unique challenges, particularly in areas of scalability, security, and observability. Microsoft Playwright has gained significant traction as a powerful automation testing framework, offering cross-browser testing capabilities, robust API handling, and advanced automation features. When combined with Jenkins, one of the most widely adopted CI/CD platforms, organizations can create powerful automated testing workflows. However, this integration introduces complex considerations around security, particularly regarding credential management, test data isolation, and report security.
Journal of Computer Engineering and Technology (JCET), 2023
Monorepos, which consolidate multiple projects into a single repository, have gained popularity f... more Monorepos, which consolidate multiple projects into a single repository, have gained popularity for their streamlined code sharing, centralized dependency management, and simplified CI/CD workflows. However, as monorepos scales, they introduce significant challenges, including performance bottlenecks, complex dependency resolution, and CI/CD inefficiencies. This article explores effective techniques for splitting monorepos into smaller, manageable repositories, such as using git filter-repo, git subtree, and manual extraction methods. The article also includes a detailed step-by-step guide for splitting repositories. A real-world case study demonstrates how decoupling a React and Angular application resolved dependency conflicts, enhanced scalability, and improved CI/CD workflows. Additionally, it outlines actionable recommendations for assessing monorepo suitability, defining clear boundaries, optimizing CI/CD, and managing team transitions. By identifying common gaps in current practicessuch as inconsistent dependency management, limited cross-repository CI/CD integration, and insufficient tooling-this paper offers practical solutions and best practices to help engineering teams optimize their repository strategies effectively.
Journal of Information Systems Engineering and Management, 2025
Domain-specific fine-tuning has become a cornerstone technique for adapting pre-trained models to... more Domain-specific fine-tuning has become a cornerstone technique for adapting pre-trained models to high-stakes and specialized tasks across sectors such as healthcare, law, finance, and software engineering. This process customizes foundational AI models by exposing them to domain-relevant data, optimizing their performance for use cases that require nuanced understanding, strict compliance, or specialized syntax. In this article, we examine the theoretical foundation of domain-specific fine-tuning, reinforced by real-world case studies across medical imaging, legal NLP, financial sentiment analysis, and code generation. Additionally, we investigate its emerging role in automated test case generation using large language models (LLMs), demonstrating how fine-tuned models can enhance software quality by producing context-aware, risk-prioritized, and regulation-compliant test cases. By analyzing different fine-tuning techniques, we identify critical considerations including data representativeness, risk of overfitting, and continuous learning. The results underscore the transformative potential of fine-tuning in making AI models both reliable and valuable for domain-specific deployment.
European Journal of Advances in Engineering and Technology, 2024
Automation frameworks are the cornerstone of efficient and scalable software testing, providing s... more Automation frameworks are the cornerstone of efficient and scalable software testing, providing structured methodologies and reusable components to enhance quality assurance and streamline processes. This article explores the essential building blocks of automation frameworks, highlighting their role in transforming testing practices through advanced tools, design patterns, and innovative approaches. By automating repetitive tasks, frameworks enable QA professionals to focus on high-value activities such as test design and usability evaluations, delivering benefits including improved accuracy, scalability, reduced manual effort, and comprehensive test coverage. Hierarchical strategies, such as the "Test Pyramid," emphasize foundational unit tests complemented by integration and end-to-end tests, optimizing testing efficiency across diverse environments. Structured frameworks-Linear Scripting, Modular, Data-Driven, Keyword-Driven, Hybrid, and Behavior-Driven Development (BDD)-cater to diverse project needs, offering flexibility and adaptability. Design patterns like Page Object Model (POM), Factory, Facade, Singleton, and Fluent POM further enhance modularity, scalability, and maintainability, ensuring robust automation architecture. Emerging methodologies, such as No-Code Test Automation, redefine accessibility through intuitive interfaces, visual workflows, and reusable components, empowering non-technical contributors to engage actively in testing processes. Integration with technologies like Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) optimizes execution, fosters collaboration, and supports advanced reporting and CI/CD pipeline integration. Despite their advantages, automation frameworks face challenges, including high setup costs, maintenance demands, and limited UX and exploratory insights. Addressing these limitations through strategic prioritization and ongoing refinement enables organizations to leverage automation frameworks as vital building blocks for delivering reliable, high-quality software. This article delves into the methodologies, components, and technologies that comprise automation frameworks, showcasing their pivotal role in driving agility, scalability, and innovation in modern software engineering.
International Journal of Science and Research (IJSR), 2025
Geographic Information Systems (GIS) play a pivotal role in enabling the collection, visualizatio... more Geographic Information Systems (GIS) play a pivotal role in enabling the collection, visualization, and analysis of spatial data across diverse industries, such as urban planning, environmental management, and disaster response. Despite their significance, GIS applications face usability challenges stemming from complex interfaces, large data volumes, and diverse user expertise. Traditional User Experience (UX) testing methods often fall short in addressing these issues due to limitations in scalability, scenario complexity, and realtime insight collection. The integration of Artificial Intelligence (AI) into UX testing introduces transformative solutions, offering automation, enhanced data analysis, and predictive insights. AI-powered tools streamline routine testing, accelerate product iteration cycles, and provide actionable design suggestions, addressing usability challenges more efficiently. Through industry case studies of tools and techniques, AI demonstrates its ability to optimize workflows, enhance navigation, and improve user interaction. Furthermore, as AI continues to advance in GIS, its application in UX testing becomes a natural extension. By leveraging AI techniques, GIS platforms can identify user pain points, refine interfaces, and enhance usability for both technical and general audiences. While AI presents unparalleled potential in GIS UX testing, challenges such as resource constraints and integration limitations persist. This article explores benefits, challenges and emphasizes the critical role of AI in creating intuitive, responsive, and accessible GIS applications.
Uploads
Papers by Twinkle Joshi