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Journal articles on the topic 'AI-Driven Testing'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Safhi, Amine el Mahdi, Gilberto Cidreira Keserle, and Stéphanie C. Blanchard. "AI-Driven Non-Destructive Testing Insights." Encyclopedia 4, no. 4 (2024): 1760–69. http://dx.doi.org/10.3390/encyclopedia4040116.

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Non-destructive testing (NDT) is essential for evaluating the integrity and safety of structures without causing damage. The integration of artificial intelligence (AI) into traditional NDT methods can revolutionize the field by automating data analysis, enhancing defect detection accuracy, enabling predictive maintenance, and facilitating data-driven decision-making. This paper provides a comprehensive overview of AI-enhanced NDT, detailing AI models and their applications in techniques like ultrasonic testing and ground-penetrating radar. Case studies demonstrate that AI can improve defect d

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Preeti, Tupsakhare, and Kulkarni Tanay. "Healthcare Technology Testing: An AI-Driven Approach." Journal of Advances in Developmental Research 16, no. 1 (2025): 1–10. https://doi.org/10.5281/zenodo.14598858.

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Artificial Intelligence (AI) is transforming the healthcare space, particularly in point-of-care testing and clinical laboratory processes. This research investigates how AI can optimize testing workflows, increase efficiency, and improve diagnostic accuracy, while navigating the stringent regulatory environment and critical demands of medical diagnostics. Key objectives include evaluating AI's current role in healthcare testing, analyzing its potential to enhance efficiency and precision, assessing challenges and limitations, and proposing integration strategies that adhere to regulatory requ

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Siva Sai Kumar Yachamaneni. "AI-driven test automation: revolutionizing enterprise integration." Global Journal of Engineering and Technology Advances 23, no. 1 (2025): 437–44. https://doi.org/10.30574/gjeta.2025.23.1.0133.

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This article examines how AI-driven test automation is transforming enterprise integration testing by addressing the limitations of traditional testing approaches. As modern organizations increasingly rely on seamless integration between diverse applications and systems, conventional testing methods struggle with maintenance burdens, limited test coverage, and inefficient execution. It explores how artificial intelligence introduces intelligence and adaptability throughout the testing lifecycle, from test design to execution and analysis. The article analyzes five core components of AI-driven

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Researcher. "THE FUTURE OF AI-DRIVEN TEST AUTOMATION." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 291–99. https://doi.org/10.5281/zenodo.14066163.

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This article explores the transformative impact of artificial intelligence (AI) on software test automation and its implications for quality assurance. It examines the rapid adoption of AI-driven testing tools across industries, highlighting key innovations such as self-healing scripts, intelligent test case generation, predictive analytics, and machine learning algorithms for test optimization. The article presents statistical evidence of AI's effectiveness in reducing testing costs, improving defect detection rates, and enhancing test coverage. It also delves into real-world applications in

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Deming, Chunhua, Md Abul Khair, Suman Reddy Mallipeddi, and Aleena Varghese. "Software Testing in the Era of AI: Leveraging Machine Learning and Automation for Efficient Quality Assurance." Asian Journal of Applied Science and Engineering 10, no. 1 (2021): 66–76. http://dx.doi.org/10.18034/ajase.v10i1.88.

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Automation and machine learning incorporated into software testing procedures are significant improvements over current quality assurance procedures. The potential of AI-driven testing methodologies to improve software testing's efficacy and efficiency is examined in this paper. The study's principal goals are investigating AI-driven testing methods, empirical assessments, case studies, identification of issues and policy consequences, and recommendations for responsible adoption. A thorough analysis of the body of research on AI-driven testing, including case studies, research papers, and pol

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Chandrika, Asha Rani Rajendran Nair. "Elevating Salesforce Testing Efficiency with Copado Robotic Testing." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–8. https://doi.org/10.55041/isjem02123.

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Salesforce testing has undergone a significant transformation, driven by the platform's rapid innovation, frequent updates, and growing implementation complexity. Traditional manual testing methods have struggled to keep pace with Salesforce’s dynamic environment, often resulting in time-intensive processes, limited coverage, inconsistent outcomes, and increased risk of human error. The need for accelerated release cycles without compromising quality has led organizations to adopt automation, enabling comprehensive regression testing and early defect detection within tighter timelines. Copado

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Niazi, Sarfaraz K. "Molecular Biosimilarity—An AI-Driven Paradigm Shift." International Journal of Molecular Sciences 23, no. 18 (2022): 10690. http://dx.doi.org/10.3390/ijms231810690.

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Scientific, technical, and bioinformatics advances have made it possible to establish analytics-based molecular biosimilarity for the approval of biosimilars. If the molecular structure is identical and other product- and process-related attributes are comparable within the testing limits, then a biosimilar candidate will have the same safety and efficacy as its reference product. Classical testing in animals and patients is much less sensitive in terms of identifying clinically meaningful differences, as is reported in the literature. The recent artificial intelligence (AI)-based protein stru

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Goti, Ankit Bharatbhai. "AI-Driven PCB Reliability Testing for IPC-9701 Compliance." International Journal of Scientific Research and Management (IJSRM) 13, no. 03 (2025): 2068–87. https://doi.org/10.18535/ijsrm/v13i03.ec03.

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The reliability of Printed Circuit Boards (PCBs) is critical in modern electronics, particularly in industries such as aerospace, automotive, and telecommunications, where failure can lead to significant operational and financial consequences. The IPC-9701 standard provides a framework for evaluating PCB reliability by testing solder joint performance under mechanical and thermal stress conditions. Traditional reliability testing methods, such as temperature cycling tests (TCT), mechanical shock tests, and vibration analysis, are labor-intensive, time-consuming, and often limited by human erro

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Yadavali, Vinaysimha Varma. "AI-driven Performance Testing Framework for Mobile Applications." International Journal of Software Engineering & Applications 15, no. 6 (2024): 33–45. https://doi.org/10.5121/ijsea.2024.15603.

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The rapid proliferation of mobile applications across diverse platforms has introduced unprecedented challenges in ensuring optimal performance under varying conditions. Traditional performance testing techniques often struggle to address the complexity of mobile environments, characterized by diverse devices, dynamic network conditions, and resource constraints. This paper presents an AI-Driven Performance Testing Framework for Mobile Applications, designed to revolutionize the way performance bottlenecks are identified and addressed. The proposed framework leverages artificial intelligence t

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Vijay Shekar, Kakumanu Naga, Sanam Veera Venkata Mani Shankar, Challa Siva Prakash, Shaik John Ussman, and B. V. V. H. Chandra Sekhar. "AI-Driven Virtual Interviewer." International Journal of Multidisciplinary Research and Growth Evaluation. 6, no. 2 (2025): 566–70. https://doi.org/10.54660/.ijmrge.2025.6.2.566-570.

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In today's competitive job market, interview preparation is an integral part of the job-seeking process, The AI Interviewer platform aims to help unemployed candidates by resolving the shortcomings of conventional interview preparation. Conventional methods are predominantly focused on knowledge-based queries, and other important factors such as communication skills, body language, and resume correctness are not handled. Current systems also fail to give customized, fine-grained feedback, which is necessary for those candidates who are interested in improving their overall interview performanc

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Singhal, Manoj Kumar, and Chhaya Gunawat. "AI-driven green testing : Optimizing efficiency and sustainability in software testing." Journal of Information and Optimization Sciences 46, no. 4-B (2025): 1347–56. https://doi.org/10.47974/jios-2001.

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Software has become integral to daily life, continually expanding with increasingly complex features to meet growing expectations. However, managing these complexities— from understanding application dependencies to ensuring reliability through rigorous testing—poses significant challenges. For organizations, delivering robust and dependable software is crucial for maintaining business success and reputation. Therefore, a significant portion of the software development life cycle is dedicated to testing. Engineers often write thousands of test cases to validate new features and prevent regress

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Deepti, Sharma, Bharti Pragya, and Kumar Sablaniya Ankit. "AI in Software Testing: Enhancing Testing Frameworks with Artificial Intelligence." Journal of Advanced Research in Artificial Intelligence & It's Applications 2, no. 2 (2025): 15–17. https://doi.org/10.5281/zenodo.14916914.

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<em>Software testing plays a crucial role in ensuring the quality and reliability of software systems. Traditional testing methods, while effective, often require significant time and manual effort, making them prone to human error. The integration of Artificial Intelligence (AI) into software testing presents a ground breaking solution by automating test case generation, execution, and defect detection. This paper explores how AI-driven automated testing frameworks are transforming the software development lifecycle, highlighting key technologies, their benefits, and real-world applications.

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Jainik Sudhanshubhai Patel. "AI-Driven Test Automation: Transforming Software Quality Engineering." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 339–47. https://doi.org/10.32996/jcsts.2025.7.2.35.

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The integration of artificial intelligence into test automation represents a paradigm shift in software quality engineering, addressing longstanding challenges of traditional testing methods. As applications grow increasingly complex with microservices architectures, cloud-native components, and frequent deployment cycles, AI-driven testing emerges as a solution to the brittleness and maintenance overhead of conventional approaches. By leveraging machine learning, natural language processing, computer vision, and self-learning systems, organizations can reduce script maintenance efforts while

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Doddapaneni, Jagan Mohan Rao. "AI Test Design and Script Generator: Enhancing Software Testing through AI-driven Automation." International Journal of Multidisciplinary Research and Growth Evaluation 6, no. 1 (2025): 1942–43. https://doi.org/10.54660/.ijmrge.2025.6.1.1942-1943.

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The evolution of software testing has led to the adoption of AI-driven tools to optimize the efficiency, accuracy, and coverage of test case generation. This paper introduces an AI Test Design and Script Generator, a cutting-edge solution that automates requirement and document reviews, enhances test coverage through generative AI, and ensures secure segmentation of customers, organizations, and departments. Unlike traditional automation tools, this AI-driven approach integrates with private Large Language Models (LLMs), providing intelligent test generation without collecting personal or sens

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Yachamaneni, Siva Sai Kumar. "The Future of AI-Driven Test Automation for Enterprise Integration." European Journal of Computer Science and Information Technology 13, no. 12 (2025): 24–33. https://doi.org/10.37745/ejcsit.2013/vol13n122433.

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Enterprise integration testing faces unprecedented challenges as organizations adopt increasingly interconnected systems and cloud services. Traditional testing approaches struggle to address these complexities, requiring excessive manual effort while delivering incomplete coverage and delayed feedback. This article explores the transformative potential of AI-driven test automation for enterprise integration testing. Through analysis of emerging innovations, including autonomous testing agents, AI-powered test orchestration, generative AI, predictive testing, cognitive automation, and self-hea

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Bo, Zang, Zang Lei, and Chang Xin. "Application of Artificial Intelligence in Spacecraft Ground Testing." Journal of Physics: Conference Series 2460, no. 1 (2023): 012171. http://dx.doi.org/10.1088/1742-6596/2460/1/012171.

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Abstract With the accelerated application of new-generation information technologies, developed countries have implemented digitalization strategies to gain a competitive advantage in the digital era. Artificial intelligence (AI) is widely used in a wide range of modern industries. Our country has put increasing emphasis on AI technologies, and our aerospace industry is being more digitalized. This study focuses on the application prospects of AI in spacecraft ground testing. It aims to build smart labs driven by products and discusses how to integrate AI into the process of quality supervisio

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Bairi, Akhil Reddy, Sarita Gahlot, and Ravi Kumar Kota. "AI-Augmented Test Automation: Enhancing Test Execution with Generative AI and GPT-4 Turbo." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 2, no. 1 (2024): 325–43. https://doi.org/10.60087/jaigs.v2i1.338.

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The rapid evolution of software development necessitates efficient and intelligent testing strategies to ensure quality and reliability. AI-augmented test automation leverages generative AI models, such as GPT-4 Turbo, to enhance test execution by improving test case generation, automated debugging, and adaptive testing processes. This paper explores the integration of AI-driven automation into software testing workflows, highlighting its advantages in accelerating test execution, reducing manual effort, and increasing test coverage. Additionally, it discusses challenges such as AI-driven test

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Joshi, Twinkle. "The Role of AI in Enhancing User Experience Testing for GIS Applications: How AI-Driven UX Testing Can Improve the Usability of Geospatial Software." International Journal of Science and Research (IJSR) 14, no. 3 (2025): 1634–42. https://doi.org/10.21275/sr25049085948.

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Srikanth, Prof Swathi, Sindhu S, Varshini S R, Sai Charan M P, and S. S. Subhash. "AI-Driven Developer Ecosystem." International Journal of Research and Scientific Innovation XII, no. VII (2025): 276–83. https://doi.org/10.51244/ijrsi.2025.120700027.

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The advent of Large Language Models (LLMs), including tools like GitHub Copilot and OpenAI Codex, has brought substantial changes to the field of software engineering. These technologies support developers through features such as automated code generation, smart code suggestions, and productivity enhancements. Despite these advancements, the development workflow is still scattered across multiple standalone tools used for coding, testing, documentation, and team communication. This lack of integration disrupts the development flow and negatively impacts overall team efficiency. To address the

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Pareek, Chandra Shekhar. "Accelerating Agile Quality Assurance with AI-Powered Testing Strategies." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem15369.

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The infusion of Artificial Intelligence (AI) into Agile software development is revolutionizing the domain of software testing, reshaping conventional methodologies to meet the demands of today’s complex and accelerated development cycles. Agile frameworks, renowned for their iterative workflows and adaptability, often encounter limitations in scaling to the velocity and intricacy of modern projects. AI emerges as a game-changer, introducing sophisticated capabilities such as hyper-automation, predictive defect analytics, and context-aware decision-making, thereby addressing these limitations

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Ms., Prajakta Sudhir Khade, and Rajeshkumar U. Sambhe Dr. "Artificial Intelligence in Software Development: A Review of Code Generation, Testing, Maintenance and Security." International Journal of Current Science Research and Review 08, no. 04 (2025): 1632–41. https://doi.org/10.5281/zenodo.15173328.

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Abstract : Artificial Intelligence (AI) is transforming software development by automating key processes such as code generation, testing, maintenance, and security. AI-powered tools like OpenAI Codex, GitHub Copilot, and DeepMind AlphaCode are revolutionizing programming by enhancing efficiency, reducing errors, and accelerating development cycles. Similarly, AI-driven testing frameworks improve bug detection, security analysis, and software performance optimization. This review explores recent advancements in AI-driven software development, analyzing its benefits, challenges, and ethical con

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Srikanth Perla. "AI-driven Test Automation for Salesforce and System Integration." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1120–29. https://doi.org/10.32628/cseit251112116.

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Integrating artificial intelligence into test automation frameworks has transformed quality assurance practices in Salesforce environments and system integrations. AI-driven solutions have revolutionized testing approaches through smart test selection, risk-based analysis, and dynamic element identification capabilities. These advancements enable organizations to detect defects earlier, reduce false positives, and significantly decrease test maintenance efforts. Self-healing locators and context-aware selection mechanisms have enhanced test stability across dynamic web applications, while patt

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Pareek, Chandra Shekhar. "From Automation to Intelligence: Revolutionizing Microservices and API Testing with AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 716–23. http://dx.doi.org/10.22214/ijraset.2024.65198.

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The shift to Microservices architecture and Application Programming Interface (API) - first development has transformed the landscape of software engineering, empowering development teams to create highly scalable, modular systems with agile, independent service deployment. However, the complexities of distributed architectures present unique challenges that traditional testing methodologies are often ill-equipped to address. These include managing inter-service dependencies, handling asynchronous communications, and ensuring data consistency across distributed nodes, all of which necessitate

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Arunkumar Yadava. "AI-driven testing frameworks for enterprise resource planning systems: A case study on oracle ERP." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2358–70. https://doi.org/10.30574/wjaets.2025.15.1.0515.

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ERP systems manage business operations at many organizational levels to streamline operations. Testing these systems becomes challenging because they combine complex structures with many modules in addition to flexible enterprise settings. The paper investigates AI-based testing frameworks for ERP systems while concentrating on the testing of Oracle ERP systems. Research analyzing the utilization of AI algorithms with predictive analysis and intelligent test code development shows how AI boosts effectiveness in ERP testing for large projects. Researchers used a case study approach to evaluate

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Leong, Wai Yie, and Sothy Sundara Raju. "Identifying Research Gaps in AI-Driven Software Testing: A Review of Automation Tools and Challenges in SMEs." ASM Science Journal 20, no. 1 (2025): 1–19. https://doi.org/10.32802/asmscj.2025.2006.

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AI-driven (Artificial Intelligence) software testing frameworks have become crucial in today's fast-paced digital environment to guarantee the timely delivery of high-quality systems. Software testing is undergoing a revolution because of the incorporation of AI-driven frameworks, which automate intricate test scenarios, improve accuracy, and drastically cut down on time-to-market. However, a lack of defined frameworks, cost concerns, and ambiguity over performance hinder the adoption of advanced AI-powered testing solutions by many SMEs (Small and Medium-Sized Enterprises). Researchers and in

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Garg, Shally. "Optimizing Test Automation: AI and ML for Smarter Software Testing." Journal of Software Engineering and Simulation 6, no. 5 (2020): 17–22. https://doi.org/10.35629/3795-06051722.

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The growing complexity of retail and e-commerce operations necessitates advanced AI-driven solutions to enhance efficiency, scalability, and decision-making. Artificial Intelligence for IT Operations (AIOps) leverages deep learning (DL) to automate IT processes, optimize supply chains, and improve customer experience. This survey reviews existing DL techniques in AIOps for retail, categorizing key challenges and exploring solutions for demand forecasting, fraud detection, and personalized recommendations. We analyze modular versus end-to-end DL architectures, offering guidelines for model sele

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Jawalkar, Santosh Kumar. "Machine Learning in QA: A Vision for Predictive and Adaptive Software Testing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 05, no. 07 (2021): 1–7. https://doi.org/10.55041/ijsrem9725.

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Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning

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Herbert, Niamh. "In what ways will AI enhance psychometric testing in the workplace?" Assessment and Development Matters 16, no. 1 (2024): 24–28. http://dx.doi.org/10.53841/bpsadm.2024.16.1.24.

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Key digested messageThis article explores how Artificial Intelligence (AI) can enhance psychometric testing in the workplace. By leveraging natural language processing, machine learning algorithms, and data analytics, AI-driven psychometric testing offers greater efficiency, accuracy, and fairness. It discusses the potential of AI to revolutionise traditional testing methods and highlights its benefits for candidate selection, talent management, and employee development.

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Wairagade, Anant, and Kimberly Morton Cuthrell. "A Systematic Review of AI-Powered Software Testing in Healthcare: Methodologies, Challenges, and Future Directions." Journal of Engineering Research and Reports 27, no. 4 (2025): 264–77. https://doi.org/10.9734/jerr/2025/v27i41470.

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AI technology brought into the field of healthcare is a matter of significant importance as it has contributed to a qualitative improvement of patient care, diagnostics as well as treatment planning. The integrity of the AI-driven healthcare applications including the accuracy, reliability, and safety aspects is what the whole game is about. Even a small matter of software bugs can have severe consequences such as a wrong diagnosis being made, the discharging of patients with the wrong medicines, or a data breach. The most common traditional testing techniques, such as manual testing and rule-

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Researcher. "ARTIFICIAL INTELLIGENCE-DRIVEN TEST CASE GENERATION TECHNIQUES FOR IMPROVING AUTOMATION ACCURACY AND COVERAGE." Journal of Software Testing (JST) 3, no. 1 (2025): 1–7. https://doi.org/10.5281/zenodo.14722887.

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The application of Artificial Intelligence (AI) to test case generation has revolutionized software testing by enhancing automation accuracy and test coverage. This paper explores the methodologies, tools, and techniques used in AI-driven test case generation. We provide an overview of the latest research, compare methods, and analyze their impact on software quality assurance. Empirical data and visual insights highlight the benefits and limitations of AI-driven methods.

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Kapoor, Saurabh. "AI-Driven Approaches to Improve Accessibility Testing Across IoT Devices." International Journal of Computer Trends and Technology 72, no. 9 (2024): 170–75. http://dx.doi.org/10.14445/22312803/ijctt-v72i9p127.

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Nusrat, Yasmin Nadia, Shadikul Bari MD, Majid Bakhsh Mohammed, Sarkar Ankur, and A. Mohaiminul Islam S. "AI-Driven Test Data Management for Large-Scale BI Applications." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 2428–38. https://doi.org/10.5281/zenodo.14874188.

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This requirement has become more so as BI applications expand in terms of functionality and volume where TDM has been deemed more important. Currently, there is a lot of hassle with regard to both the generation, management and validation of test data used in traditional testing processes, that does not adequately address the adaptive requirements of contemporary BI solutions leading to decreased efficiency, inadequate quality and incoherent outcomes as well as unrealistic data fidelity. Finally, this article aims to look at how AI technology can rise to the occasion of these Alarge-scale BI a

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Vikram Sai Prasad Karnam. "AI and machine learning driven test automation: Revolutionizing software testing practices." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1560–71. https://doi.org/10.30574/wjaets.2025.15.2.0700.

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The integration of Artificial Intelligence and Machine Learning into software testing processes represents a transformative advancement in quality assurance practices. This technical article examines how AI-driven testing is revolutionizing traditional approaches through adaptive capabilities that respond dynamically to application changes. These intelligent systems introduce self-healing test scripts that automatically adapt to UI modifications, generate comprehensive test cases through sophisticated algorithms, and predict potential defects before they manifest in production environments. Ac

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Aswinkumar Dhandapani. "AI-driven test automation for microservices: Advancing quality assurance in distributed systems." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1868–81. https://doi.org/10.30574/wjaets.2025.15.2.0715.

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Artificial intelligence is changing how we test microservices in ways that traditional methods simply can't match. When companies shift from big, unified systems to smaller, distributed services, testing becomes much more complex - services depend on each other, they communicate asynchronously, and they often use different technologies. AI testing tools use machine learning, natural language processing, and other advanced techniques to generate smarter tests, spot unusual behaviors, and run tests more efficiently. When combined with existing DevOps practices, these AI approaches are better at

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Priya Yesare. "Revolutionizing Automation Testing with AI: A New Era of Intelligent Quality Assurance." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 626–33. https://doi.org/10.32628/cseit25112395.

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Automation testing using AI is replacing the conventional testing procedures by optimizing efficiency, accuracy and defect detection. Traditional automation testing is based on the scripts but the prediction of analytics and machine learning is used in AI powered framework to optimize the test execution. The focus of this research is the effects that AI driven automation has for defect leakage reduction, test maintenance cost reduction, and adaptability. The leakage rate defect reduction is found to be 66%, while the cost reduction is 60%. Integration of AI into testing frameworks help organiz

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Khinvasara, Tushar, Abhishek Shankar, and Connor Wong. "Robustness and Reliability Testing in Healthcare Using Artificial Intelligence." Asian Journal of Research in Computer Science 17, no. 7 (2024): 103–18. http://dx.doi.org/10.9734/ajrcos/2024/v17i7482.

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Testing the security, efficiency, and dependability of AI-driven healthcare systems is crucial. It is essential to perform thorough and rigorous testing to make sure the AI algorithms are capable. Our goal is to ensure that these algorithms can handle a wide range of scenarios that may occur in healthcare settings. We must observe, for instance, how well they function in the presence of changes in patient characteristics, data accuracy, and even environmental factors. Developers are able to go deeply and find any potential flaws, biases, or restrictions by thoroughly testing AI models. This en

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Pradeep, Kumar. "Adaptive Workload Modeling using AI for Performance Testing of Cloud-Based Multitenant Enterprise Applications." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 1 (2024): 1–17. https://doi.org/10.5281/zenodo.15087595.

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Cloud-based multitenant enterprise applications face growing challenges in optimizing performance, managing resources efficiently, and ensuring scalability due to unpredictable workload fluctuations. Traditional workload management approaches, such as rule-based and threshold-based autoscaling, struggle to accurately forecast and respond to dynamic workload variations, leading to higher latency, inefficient resource utilization, and increased operational costs. To address these challenges, this paper introduces an AI-driven adaptive workload modeling framework that leverages machine learning (

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Gopinath Kathiresan. "Automated Test Case Generation with AI: A Novel Framework for Improving Software Quality and Coverage." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2880–89. https://doi.org/10.30574/wjarr.2024.23.2.2463.

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Modern software testing has become imperative as testing is being automated test case generation: it makes the test efficient, accurate and completely covered. Traditionally, scalability, adaptability, and completeness are the Achilles heels of scalability of traditional testing methods as manual and scripted. In this paper, we introduce a novel AI driven framework for automated test case generation based on deep learning and reinforcement learning using evolutionary algorithm to improve test case generation process. It provides an effective test coverage by dynamically generating and prioriti

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Naisargi, Oza, and Punit Bhope Divya. "GENERATIVE AI IN THE SOFTWARE DEVELOPMENT LIFECYCLE (SDLC): OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS." International Educational Applied Research Journal 09, no. 03 (2025): 183–95. https://doi.org/10.5281/zenodo.15231434.

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Generative Artificial Intelligence (AI) is fundamentally transforming the Software Development Lifecycle (SDLC) by enabling automation in code generation, enhancing software quality assurance, and streamlining development workflows. A detailed examination of the integration of Generative AI across all major phases of the SDLC, including requirements engineering, architectural and system design, implementation, testing, deployment, and post-deployment maintenance. A methodical assessment of state-of-the-art AI-assisted development tools—such as GitHub Copilot, ChatGPT, and Tabnine—i

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Pradeepkumar Palanisamy. "AI-driven predictive testing: Enhancing software reliability in high-stakes financial systems." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 3791–98. https://doi.org/10.30574/wjarr.2025.26.1.1451.

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This article explores how AI-driven predictive testing is transforming software quality assurance in high-stakes financial systems. Traditional testing methods remain reactive, identifying defects only after they manifest, whereas predictive testing leverages machine learning to anticipate and prevent failures before they occur. The article examines the evolution from conventional to AI-powered testing approaches, detailing core components of predictive testing frameworks, including failure analysis using historical data, dynamic test case prioritization, and automated root cause analysis. Imp

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Naimil, Navnit Gadani. "The Future of Software Development: Integrating AI and Machine Learning into the SDLC." International Journal of Engineering and Management Research 14, no. 1 (2024): 308–15. https://doi.org/10.5281/zenodo.13756677.

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The integration of AI and ML into the SDLC represents a groundbreaking advancement in software engineering. This paper explores the transformative effects of AI-driven automation on key stages of the SDLC, including code generation, testing, and deployment. It also examines architectural frameworks that support the effective integration of AI technologies, such as Microservices Architecture, Event-Driven Architecture, and Hybrid Cloud Architecture. By analyzing quantitative improvements and discussing future research directions, the paper provides a comprehensive overview of how AI and ML are

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Veerendeswari J, Keerthana Priya M, Shushmita P, and Varsha S S. "AI-Driven Decision Making in Business Analytics." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1857–63. https://doi.org/10.47392/irjaeh.2025.0269.

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Machine learning has the potential to transform industries, but many small and medium-sized enterprises (SMEs) struggle with the technical demands of building optimized models. To solve this, we propose an user-friendly framework powered by Automated Machine Learning (AutoML) tools. TPOT helps automate the complex task of choosing the right algorithms and hyperparameter tuning their settings, while PyCaret simplifies data preprocessing tasks such as feature engineering, class imbalance handling, and encoding. and allows quick testing of different models. Together, these tools make the entire m

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Lazar Stоšić and Elena N. Malyuga. "APPLICATION OF ARTIFICIAL INTELLIGENCE IN LANGUAGE SKILLS TESTING." ANGLISTICUM. Journal of the Association-Institute for English Language and American Studies 13, no. 1 (2024): 22–34. http://dx.doi.org/10.58885/ijllis.v13i1.22ls.

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The integration of artificial intelligence (AI) into language testing marks a transformative shift in how we evaluate linguistic capabilities. AI-driven tools offer unparalleled precision, personalization, and innovative assessment methods, revolutionizing language assessment’s accuracy and adaptability. Through advanced algorithms and tailored approaches, AI ensures precise skill evaluation, customized testing, and individualized improvement strategies, promising a more effective language learning experience. However, alongside these advancements, ethical considerations arise concerning data

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Azhar Shaikh, Mohammad Aqdas. "AI-Driven Real-Time Phishing Detection System." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04419.

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ABSTRACT: The escalating threat of phishing attacks poses significant risks to individuals and organizations, compromising sensitive data and financial security. Traditional phishing detection methods, reliant on manual analysis and static rules, struggle to keep pace with the sophistication and volume of modern attacks. This study proposes an innovative artificial intelligence (AI)-driven system for real-time phishing detection, integrating machine learning algorithms with a dynamic network of data sources, including email metadata, URL features, and user behavior analytics. By leveraging adv

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Hote, Shital. "A Survey Paper Review on Advancements in AI Driven User Interface Testing." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 674–79. http://dx.doi.org/10.22214/ijraset.2024.57902.

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Abstract: This survey explores the quality of software engineering and highlights the important role of artificial intelligence (AI) in improving software testing. It emphasizes the importance of software testing to determine the effectiveness and capabilities of software programs. This paper highlights inconsistencies in measurement guidance and the need for automation. It will also provide a better look at the changing ecosystem of automation products driven by roles in the convergence of the artificial intelligence and machine learning (ML) eras. An AI-powered machine is made based on machi

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Matluck Ayomide Afolabi, Henry Chukwuemeka, Olisakwe, and Thompson Odion Igunma. "Catalysis 4.0: A framework for integrating machine learning and material science in catalyst developmentpna." Global Journal of Research in Multidisciplinary Studies 2, no. 2 (2024): 038–46. http://dx.doi.org/10.58175/gjrms.2024.2.2.0053.

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This paper introduces Catalysis 4.0, a comprehensive framework for leveraging artificial intelligence (AI) and machine learning (ML) in the development of catalytic materials. The framework is structured around three key components: data-driven material discovery, virtual testing environments, and adaptive feedback loops. Data-driven material discovery utilizes AI algorithms to predict catalytic performance based on extensive material properties and high-quality datasets. Virtual testing environments provide simulation platforms to evaluate catalyst efficiency under various industrial conditio

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Pareek, Chandra Shekhar. "Self-Learning Test Orchestration for Continuous AI Validation in InsurTech." Journal of Frontiers in Multidisciplinary Research 6, no. 1 (2025): 48–54. https://doi.org/10.54660/.ijfmr.2025.6.1.48-54.

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The rapid advancement of artificial intelligence (AI) in the InsurTech industry is transforming the way insurance services are delivered, making processes more efficient, personalized, and data driven. However, this progress also brings new challenges, particularly in ensuring the accuracy, reliability, and compliance of these AI-driven systems. Traditional testing methods often struggle to keep pace with the dynamic nature of AI algorithms, evolving datasets, and ever-changing regulatory landscapes. In response to these challenges, this paper delves into the concept of self-learning test orch

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Ravipudi, Swetha, Akhil Reddy Bairi, and Ravi Kumar Burila. "5G-Driven Edge Test Automation: AI-Orchestrated Cypress and Playwright Framework for Distributed Cloud Testing." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 4, no. 1 (2024): 405–18. https://doi.org/10.60087/jaigs.v4i1.340.

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The rapid evolution of 5G networks has significantly transformed software testing paradigms, particularly in cloud-based and edge computing environments. This research explores the integration of AI-driven automation within Cypress and Playwright frameworks to enhance distributed cloud testing in 5G-powered ecosystems. By leveraging artificial intelligence for test orchestration, the proposed approach optimizes test execution efficiency, reduces latency, and ensures high reliability across diverse edge environments. The study evaluates the effectiveness of AI-orchestrated automation in handlin

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Subham Dandotiya. "Generative AI for software testing: Harnessing large language models for automated and intelligent quality assurance." International Journal of Science and Research Archive 14, no. 1 (2025): 1931–35. https://doi.org/10.30574/ijsra.2025.14.1.0266.

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Software testing is indispensable for ensuring that modern applications meet rigorous standards of functionality, reliability, and security. However, the complexity and pace of contemporary software development often overwhelm traditional and even AI-based testing approaches, leading to gaps in coverage, delayed feedback, and increased maintenance costs. Recent breakthroughs in Generative AI, particularly Large Language Models (LLMs), offer a new avenue for automating and optimizing testing processes. These models can dynamically generate test cases, predict system vulnerabilities, handle cont

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Bipin Gajbhiye, Anshika Aggarwal, and Shalu Jain. "Automated Security Testing in DevOps Environments Using AI and ML." International Journal for Research Publication and Seminar 15, no. 2 (2024): 259–71. http://dx.doi.org/10.36676/jrps.v15.i2.1472.

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The rapid adoption of DevOps practices has transformed the software development landscape by emphasizing continuous integration, continuous delivery (CI/CD), and agile methodologies. However, this rapid pace of development often introduces significant security challenges, as traditional security testing methods struggle to keep up with the accelerated release cycles. To address these challenges, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into automated security testing has emerged as a promising solution. This paper explores the use of AI and ML to enhance automa

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