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Littérature scientifique sur le sujet « AI-Driven Testing »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Articles de revues sur le sujet "AI-Driven Testing"

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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Thèses sur le sujet "AI-Driven Testing"

Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.

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Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training da

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Livres sur le sujet "AI-Driven Testing"

Frontend Web Developer's Guide to Testing: Explore Leading Web Test Automation Frameworks and Their Future Driven by Low-Code and AI. de Gruyter GmbH, Walter, 2022.

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a Frontend Web Developer's Guide to Testing: Explore Leading Web Test Automation Frameworks and Their Future Driven by Low-Code and AI. Packt Publishing, Limited, 2022.

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Shneiderman, Ben. Human-Centered AI. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192845290.001.0001.

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Researchers, developers, business leaders, policy makers, and others are expanding the technology-centered scope of artificial intelligence (AI) to include human-centered AI (HCAI) ways of thinking. This expansion from an algorithm-focused view to embrace a human-centered perspective can shape the future of technology so as to better serve human needs. Educators, designers, software engineers, product managers, evaluators, and government agency staffers can build on AI-driven technologies to design products and services that make life better for people and enable people to care for each other.

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Chapitres de livres sur le sujet "AI-Driven Testing"

Sabané, Aminata, Laura Plein, and Tegawendé F. Bissyandé. "Leveraging Conversational AI for Accelerating User-Driven Software Testing." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81573-7_6.

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Mock, Moritz, Jorge Melegati, and Barbara Russo. "Generative AI for Test Driven Development: Preliminary Results." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-72781-8_3.

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AbstractTest Driven Development (TDD) is one of the major practices of Extreme Programming for which incremental testing and refactoring trigger the code development. TDD has limited adoption in the industry, as it requires more code to be developed and experienced developers. Generative AI (GenAI) may reduce the extra effort imposed by TDD. In this work, we introduce an approach to automatize TDD by embracing GenAI either in a collaborative interaction pattern in which developers create tests and supervise the AI generation during each iteration or a fully-automated pattern in which developer

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Alam, Md Mahbub, Sabrina Islam Priti, Selim Ahmed, Kanij Fatema, Mahady Hasan, and Nujhat Nahar. "AI-Driven Solutions for Regression Testing: Insights from Bangladesh Software Industry." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1210-9_43.

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Cronin, Neil, Ari Lehtiö, and Jussi Talaskivi. "Research for JYU: An AI-Driven, Fully Remote Mobile Application for Functional Exercise Testing." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_18.

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AbstractAs people live longer, the incidence and severity of health problems increases, placing strain on healthcare systems. There is an urgent need for resource-wise approaches to healthcare. We present a system built using open-source tools that allows health and functional capacity data to be collected remotely. The app records performance on functional tests using the phone’s built-in camera and provides users with immediate feedback. Pose estimation is used to detect the user in the video. The x, y coordinates of key body landmarks are then used to compute further metrics such as joint a

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Kalpakis, George, Caterina Paternoster, Marina Mancuso, et al. "AI-Based Framework for Supporting Micro and Small Hosting Service Providers on the Report and Removal of Online Terrorist Content." In Security Informatics and Law Enforcement. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62083-6_20.

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AbstractThis work proposes a framework integrating AI technologies and tools to assist micro and small hosting service providers (HSPs) in adhering to the terrorist content online (TCO) Regulation. The framework encompasses: (i) a suite of AI tools for the automated detection and removal of TCO, (ii) a federated learning infrastructure for (re-)training and testing the underlying AI models, (iii) a secure shared hash repository to facilitate the automated prevention of uploading subversive content to HSP platforms, and (iv) a unified reporting mechanism to support the submission of removal ord

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Winkler, Ulrich. "AI-Driven E2E Testing and Cucumber Test Generation: A GPT-Powered Approach for Improved Software Quality and Collaboration." In Lecture Notes in Electrical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61221-3_20.

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Wahid, Kareem A., Cem Dede, Dina M. El-Habashy, et al. "Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83274-1_1.

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Abstract Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challeng

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Pezeshk, Sara, and Shahin Vassigh. "A Symbiotic Approach for Developing Shoreline Infrastructure." In Computational Design and Robotic Fabrication. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3433-0_5.

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Abstract This research project presents an alternative approach to addressing the complex challenges of sustainability of the coastlines by integrating advanced technology solutions with ecological conservation principles. The paper introduces the Ecoblox, a modular infrastructure system consisting of interlocking blocks devised for attachment to seawalls to improve marine biodiversity at the water edge. The design of the Ecoblox system employs environmental data, data analytics, AI-powered generative algorithms, and digital fabrication to produce blocks with complex shapes and textures suitab

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Choiński, Mateusz, Mateusz Rogowski, Piotr Tynecki, Dries P. J. Kuijper, Marcin Churski, and Jakub W. Bubnicki. "A First Step Towards Automated Species Recognition from Camera Trap Images of Mammals Using AI in a European Temperate Forest." In Computer Information Systems and Industrial Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84340-3_24.

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AbstractCamera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Białowieża Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open-source application for

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Singh, Pranav, Nirdesh Tanwar, Nitin Singh, and Sachin Sharma. "AI-Driven Continuous Integration." In Advances in Public Policy and Administration. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-8069-7.ch002.

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The integration of AI-driven solutions within Continuous Integration and Delivery (CI/CD) pipelines has transformed software development practices, offering enhanced automation, efficiency, and scalability. This paper explores how AI-powered CI/CD pipelines can significantly boost developer productivity, particularly in the context of blue-green infrastructure deployments and sustainable digital transformation initiatives. By automating code testing, deployment, monitoring, and decision-making processes, AI streamlines development cycles, minimizes errors, and ensures faster release times. The

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Actes de conférences sur le sujet "AI-Driven Testing"

Huang, Yu, Alex Yu, Louis Liu, and Xijiang Lin. "AI Driven Testing." In 2024 IEEE International Test Conference in Asia (ITC-Asia). IEEE, 2024. http://dx.doi.org/10.1109/itc-asia62534.2024.10661318.

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Saicharan, Thornala Omkar, Ramireddy Devendra Reddy, Patlolla Vivek Reddy, Godavarthi Ajay Chowdary, K. Sivakrishna, and Gowrisankar Kalakoti. "AI-Driven Parcel Delivery System." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11052929.

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Sheeba, R., Dishu Mahajan, Ayush Thakur, Syed Zohaib Karim, P. Chinnasamy, and Manjunathan Alagarsamy. "AI-Driven Predictive Resource Allocation for Disaster Response." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11052926.

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Paeleke, Leonard, Navid Keshtiarast, Paul Seehofer, et al. "Demo: Testing AI-Driven Mac Learning in Autonomic Networks." In 2024 IEEE 32nd International Conference on Network Protocols (ICNP). IEEE, 2024. https://doi.org/10.1109/icnp61940.2024.10858521.

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Ma, Junbang. "AI-Driven Web Application Automated Testing and Code Optimization." In 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025. https://doi.org/10.1109/ainit65432.2025.11035018.

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Helmy, Mona, Omar Sobhy, and Farida ElHusseiny. "AI-Driven Testing: Unleashing Autonomous Systems for Superior Software Quality Using Generative AI." In 2024 International Telecommunications Conference (ITC-Egypt). IEEE, 2024. http://dx.doi.org/10.1109/itc-egypt61547.2024.10620598.

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Paduraru, Ciprian, Miruna Zavelca, and Alin Stefanescu. "Agentic AI for Behavior-Driven Development Testing Using Large Language Models." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013374400003890.

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Rajesh, Akshaya, and Sumbul Khan. "Learn Like Feynman: Developing and Testing an AI-Driven Feynman Bot." In 2024 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE). IEEE, 2024. https://doi.org/10.1109/tale62452.2024.10834370.

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Donvir, Anujkumarsinh, and Gaurav Sharma. "Ethical Challenges and Frameworks in AI-Driven Software Development and Testing." In 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2025. https://doi.org/10.1109/ccwc62904.2025.10903892.

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Pugazhenthi, Vigneshwaran Jagadeesan, Gokul Pandy, Baskaran Jeyarajan, and Aravindhan Murugan. "AI-Driven Voice Inputs for Speech Engine Testing in Conversational Systems." In SoutheastCon 2025. IEEE, 2025. https://doi.org/10.1109/southeastcon56624.2025.10971485.

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