Journal articles: 'Cloud-Based Testing'

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Wangyal, Rinzen, and Dr Murugan R. "Cloud Based COVID-19 Testing Management System." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 2347–48. http://dx.doi.org/10.22214/ijraset.2022.41125.

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Abstract: Nowadays, COVID19 Testing Management System is one of the most essential tools that are mostly used in Testing Lab; it is mostly used to manage COVID19 medical lab related activities. In this project we tried to develop a computerized and web-based Cloud COVID19 Testing management system. Our main intention is to allow this application to be used in most retailing COVID19 lab, where a small point of customization will be required to each COVID19 lab in the implementation period. This system is designed to overcome all challenges related to the management of diagnostic that were used to be handled locally and manually. The system is an online COVID19 lab manager application that brings up various COVID19 test working online. Using this system, it will help us to records all transaction made at the daily tests; recognize all customers, employees, etc. It will manage all activities around the COVID19 lab that increases productivity and maximize profit, it will also be minimizing the risk of getting loss because all transactions are recorded to the system.

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Priyanka, Inderveer Chana, and Ajay Rana. "Empirical evaluation of cloud-based testing techniques." ACM SIGSOFT Software Engineering Notes 37, no. 3 (2012): 1–9. http://dx.doi.org/10.1145/2180921.2180938.

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Kılınç, Nergiz, Leyla Sezer, and lok Mishra. "Cloud-Based Test Tools: A Brief Comparative View." Cybernetics and Information Technologies 18, no. 4 (2018): 3–14. http://dx.doi.org/10.2478/cait-2018-0044.

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Abstract The concept of virtualization has brought life to the new methods of software testing. With the help of cloud technology, testing has become much more popular because of the opportunities it provides. Cloud technologies provides everything as a service, hence the software testing is also provided as a service on cloud with the privileges of lower cost of testing, and relatively less effort. There are various cloud-based test tools focusing on different aspects of software testing such as load tests, regression tests, stress tests, performance tests, scalability tests, security tests, functional tests, browser performance tests, and latency tests. This paper investigates the cloud-based testing tools focusing on different aspects of software testing.

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Santhosh S, Et al. "A Structured Cloud-Based Software Testing Model with a Case Study Implementation." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1601–13. http://dx.doi.org/10.17762/ijritcc.v11i10.8712.

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Cloud-based testing methodologies were gaining significant popularity and adoption in the software testing industry. Cloud-based testing offers several advantages, such as scalability, flexibility, cost-effectiveness, and the ability to access a wide range of testing tools and environments without the need for extensive infrastructure setup. Cloud testing methods are having challenges with respect to testing priority, practical use cases, performance, lengthy test time, integrating and streamlining, data security, etc. since they are addressing specific purposes. To address these challenges, there is a need for a structured testing model with respect to the cloud environment. This article proposes a new structured cloud-based testing model for enhancing the testing service in the cloud environment. The proposed model addresses the order of testing and the priority, data security, and performance by using Smoke and Sanity testing methods.

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Al-Khannak, Rafid, and Sajjan Singh Nehal. "Penetration Testing for the Cloud-Based Web Application." WSEAS TRANSACTIONS ON COMPUTERS 22 (August 29, 2023): 104–13. http://dx.doi.org/10.37394/23205.2023.22.13.

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This paper discusses methods, tools, approaches, and techniques used for the penetration testing on the cloud-based web application on Amazon AWS platform. The findings of a penetration test could be used to fix weaknesses and vulnerabilities, and significantly improve security. The testing is implemented by undertaking a malicious attack aiming to breach system networks and thereby confirm the presence of cloud infrastructure. The research focuses on cloud-based web applications' high-risk vulnerabilities such as unrestricted file upload, command injection, and cross-site scripting. The outcomes expose and approved some vulnerabilities, flaws, and mistakes in the utilised cloud based web application. It is concluded that some vulnerabilities haveto be considered before architecting the cloud system. Recommendations are proposing solutions to testing results.

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ANTONIA, BERTOLINO, DE ANGELIS GUGLIELMO, GALLEGO MICAEL, et al. "A Systematic Review on Cloud Testing." ACM Computing Surveys 52, no. 5 (2019): 93:1——93:42. https://doi.org/10.1145/3331447.

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A systematic literature review is presented that surveyed the topic of cloud testing over the period 2012–2017. Cloud testing can refer either to testing cloud-based systems (testing of the cloud) or to leveraging the cloud for testing purposes (testing in the cloud): both approaches (and their combination into testing of the cloud in the cloud) have drawn research interest. An extensive paper search was conducted by both automated query of popular digital libraries and snowballing, which resulted in the final selection of 147 primary studies. Along the survey, a framework has been incrementally derived that classifies cloud testing research among six main areas and their topics. The article includes a detailed analysis of the selected primary studies to identify trends and gaps, as well as an extensive report of the state-of-the-art as it emerges by answering the identified Research Questions. We find that cloud testing is an active research field, although not all topics have received enough attention and conclude by presenting the most relevant open research challenges for each area of the classification framework.

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Zulkifli, Dr Nurul H., and Dr Farah M. Rahimi. "ACCOUNTABLE DATA AUTHORIZATION IN CLOUD ENVIRONMENTS: AN IDENTITY-BASED ENCRYPTION FRAMEWORK WITH EQUALITY TESTING." International Journal of Modern Computer Science and IT Innovations 2, no. 1 (2025): 1–7. https://doi.org/10.55640/ijmcsit-v02i01-01.

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Ensuring secure and accountable access control in cloud environments is critical to protecting sensitive data from unauthorized use. This paper presents an identity-based encryption (IBE) framework enhanced with equality testing capabilities to support accountable data authorization. The proposed model allows data owners to encrypt information based on user identities while enabling controlled equality checks on ciphertexts without compromising data confidentiality. Additionally, a built-in accountability mechanism enables traceability of malicious activities and misuse of access privileges. Security and performance evaluations demonstrate that the framework provides strong data protection, efficient query operations, and practical enforcement of data accountability in distributed cloud systems.

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Maheswara, Reddy Basireddy. "Cloud Computing: Strategies for Data Storage, Containerization, CI/CD, and Testing in Cloud Environments." European Journal of Advances in Engineering and Technology 8, no. 3 (2021): 48–58. https://doi.org/10.5281/zenodo.11408101.

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Because cloud computing offers cost-effectiveness, scalability, and on-demand access to computer resources, it has completely changed how businesses handle these resources. The data storage services provided by the main cloud platforms, containerization technologies for application deployment, continuous integration and deployment (CI/CD) procedures, and testing and maintenance techniques in cloud environments are just a few of the aspects of cloud computing strategies that are examined in this research paper. The first section of the article evaluates and discusses the capabilities, use cases, and selection criteria of the database and storage services offered by Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). After that, it explores containerization technologies like Docker and Kubernetes, which make it easier to package, launch, and manage cloud applications. In addition, the study looks at best practices, technologies, and ideas related to continuous integration and delivery (CI/CD), highlighting the value of automating software delivery pipelines for accelerated time-to-market, enhanced teamwork, and dependable deployments. It also looks at different approaches, techniques, tools, logging, monitoring, scalability issues, and maintenance procedures specific to cloud-based apps.

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Liu, Shu Kun, Jin Peng Tang, Ji Feng Chen, et al. "Virtual Software Testing Service Based on Cloud Computing." Applied Mechanics and Materials 529 (June 2014): 739–42. http://dx.doi.org/10.4028/www.scientific.net/amm.529.739.

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Clouding computing and virtualization technology have brought immense effects to software organizations including software testing region. At the same time the method and related technology will be changed a lot. Some concepts, structures and characters are described in this paper. Software testing should be regarded as a service. Four typical patterns of cloud testing and the relations among them are showed in the paper. Combined with the virtualization technology, the frame of TaaS (Testing as a Service) is defined too. In the meantime, cloud testing and traditional software testing is compared with each other. At last, the main research contents about software testing in virtualization environment are summarized.

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Pavan, Kumar Gollapudi. "Cloud-Based Testing for Guidewire Applications: Scalability and Performance." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 12 (2025): 2400–2406. https://doi.org/10.5281/zenodo.14603653.

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Cloud-based testing for Guidewire applications focuses on enhancing scalability and performance to address the complexities of policy and claims management systems. By leveraging cloud platforms like AWS and Azure, dynamic resource allocation, real-time analytics, and high-volume test execution are achieved. The approach ensures robust validation of Guidewire modules, including PolicyCenter, ClaimCenter, and BillingCenter, enabling faster releases and optimized performance.  AIM: To develop a scalable and efficient cloud-based testing framework for Guidewire applications that ensures comprehensive test coverage, seamless integration, and high system reliability during high-demand operations.  Study Design: The study utilized automated regression, API, and performance testing to evaluate Guidewire’s scalability and reliability. It emphasized real-time resource management in cloud environments and iterative testing to adapt to system updates. Tools like JMeter, Selenium, and Postman were used to execute end-to-end tests while analyzing their impact on performance.  Place and Duration of Study:  Place: Conducted in a cloud-based environment utilizing AWS and Azure platforms to replicate real-world operational conditions. The distributed study enabled remote collaboration among QA teams globally.  Duration: Spanned 6 months, divided into setup (1 month), iterative testing (4 months), and results analysis and optimization (1 month)  Methodology: The methodology for cloud-based testing of Guidewire applications involves dynamic cloud resource provisioning, automated tests for UI, API, and load validation, real-time monitoring, and iterative optimization. Tools like AWS, Selenium, Postman, and JMeter ensure scalability, while  CI/CD pipelines integrate continuous testing for enhanced performance.  Conclusion: Cloud-based testing for Guidewire applications delivers scalability, agility, and superior performance validation. By integrating dynamic resource management, automated testing tools, and real-time analytics, the approach reduces operational risks, accelerates deployments, and ensures robust system functionality under dynamic loads. This method proves essential for modernizing insurance systems.

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Yuan, Haigen, and Hongli Li. "Framework of Software Testing Based on Cloud Computing." Journal of Physics: Conference Series 1345 (November 2019): 052005. http://dx.doi.org/10.1088/1742-6596/1345/5/052005.

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Anitha, K. L., and R. Gopalakrishnan Nair T. "Online cloud performance testing in social networks at peak demand scenarios." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 1 (2020): 372–78. https://doi.org/10.11591/ijeecs.v17.i1.pp372-378.

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Cloud computing assures to deliver reliable services through advanced data centers built on virtualized compute and storage technologies. Users demanding more cloud services will be able to access applications and data from a Cloud anywhere in the world in a pay-as-you-go model. In this paper, we focus on cloud-based performance testing for the applications. We use Load Storm testing tool to configure and test plans to measure the performance of web applications in online social networks. The experimental observations designed to assess the performance fluctuations of social networks on maximum consumer demand days have given specific data pointers which could be utilized for further studies of web service enhancements.

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Narendar, Kumar Ale. "Leveraging AWS and Azure for Cloud-Based Test Automation: Techniques, Challenges, and Future Prospects." European Journal of Advances in Engineering and Technology 8, no. 7 (2021): 93–95. https://doi.org/10.5281/zenodo.12754762.

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Cloud platforms like Amazon Web Services (AWS) and Microsoft Azure have revolutionized the landscape of test automation by providing scalable, flexible, and cost-effective solutions. This paper explores the techniques used in leveraging AWS and Azure for cloud-based test automation, identifies the challenges faced in their implementation, and discusses the prospects of these technologies in enhancing software testing efficiency.

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Pradeepkumar Palanisamy. "Cloud-based and containerized testing environments: Revolutionizing test automation." International Journal of Science and Research Archive 4, no. 1 (2021): 352–62. https://doi.org/10.30574/ijsra.2021.4.1.0126.

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The increasing adoption of cloud computing and containerization technologies (such as Docker and Kubernetes) profoundly impacted test automation. This article investigates the advantages and implementation strategies of leveraging cloud-based and containerized environments for software testing. It highlights how these technologies provided unparalleled scalability, flexibility, and cost-efficiency for creating on-demand, isolated, and consistent test environments. The article discusses the benefits for parallel test execution, environment provisioning, and managing diverse test configurations, enabling organizations to perform comprehensive testing across various platforms and scenarios with greater agility and reduced infrastructure overhead. Furthermore, it addresses the challenges and considerations for successful adoption, solidifying the role of cloud and containers as foundational elements for modern, efficient, and reliable test automation.

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Viharika Bhimanapati, Shalu Jain, and Om Goel. "Cloud-Based Solutions for Video Streaming and Big Data Testing." Universal Research Reports 10, no. 4 (2023): 329–45. http://dx.doi.org/10.36676/urr.v10.i4.1333.

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In the era of digital transformation, cloud-based solutions have revolutionized the video streaming industry and big data testing methodologies. As consumer demand for high-quality, on-demand video content surges, traditional on-premises infrastructure often falls short in meeting scalability, performance, and cost-efficiency requirements. Cloud-based platforms, with their scalable resources and flexibility, have become essential in addressing these challenges. This paper explores the benefits and implementation strategies of cloud-based solutions for video streaming and big data testing, focusing on how these technologies enhance performance, scalability, and cost-effectiveness Video streaming services require a robust infrastructure capable of handling large volumes of data and delivering seamless user experiences across diverse devices and network conditions. Cloud-based video streaming solutions offer several advantages, including the ability to dynamically scale resources based on user demand, optimize content delivery through content delivery networks (CDNs), and provide a global reach without the need for extensive physical infrastructure. By leveraging cloud services, streaming platforms can reduce latency, enhance video quality, and improve overall user satisfaction. Traditional testing methods often struggle to cope with the complexity and volume of big data. Cloud-based testing platforms provide scalable environments where testers can simulate various data scenarios, execute performance tests, and analyze results with greater efficiency. The cloud's elasticity allows for on-demand provisioning of testing resources, making it easier to handle the dynamic nature of big data and conduct comprehensive testing without investing in expensive hardware.

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Chen, Min Gang, Wen Bin Zhong, Wen Jie Chen, Yun Hu, and Li Zhi Cai. "A Web Automation Testing Framework over Cloud." Applied Mechanics and Materials 556-562 (May 2014): 6149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6149.

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With the increasingly fast-paced software releasing or updating, research on the method of an efficient software automation testing framework based on cloud computing has become particularly important. In this paper, we propose an automation testing framework over cloud. We also describe some key technologies in the aspect of the design of hierarchical test case and automatic distribution of test cases in the cloud computing environment. Testing experiments show that our framework can take advantage of on-demand testing resources in the cloud to improve the efficiency of automation testing.

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Benkhelifa, Elhadj. "Cloud-based development life cycle: Software testing as service." Qatar Foundation Annual Research Forum Proceedings, no. 2013 (November 2013): ICTP 019. http://dx.doi.org/10.5339/qfarf.2013.ictp-019.

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Jin, Wenming, Ju Qian, and Shuoyan Yan. "Shared-Mode Resource Allocation for Cloud-Based Load Testing." IEEE Access 8 (2020): 161894–907. http://dx.doi.org/10.1109/access.2020.3020863.

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Gang, Wu, Zhao Chao, Zhao Jing, Zhang Fan, Wang Haitao, and Cao Xinyu. "Proficiency Testing Statistics Analysis System Based on Cloud Computing." IOP Conference Series: Materials Science and Engineering 439 (November 5, 2018): 032088. http://dx.doi.org/10.1088/1757-899x/439/3/032088.

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Riungu-Kalliosaari, Leah, Ossi Taipale, Kari Smolander, and Ita Richardson. "Adoption and use of cloud-based testing in practice." Software Quality Journal 24, no. 2 (2014): 337–64. http://dx.doi.org/10.1007/s11219-014-9256-0.

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Baride, Srikanth, and Kamlesh Dutta. "A cloud based software testing paradigm for mobile applications." ACM SIGSOFT Software Engineering Notes 36, no. 3 (2011): 1–4. http://dx.doi.org/10.1145/1968587.1968601.

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Valtari, Jani, Hanna-Mari Aalto, Tuan Vu, and Heikki Paananen. "Optimisation of secondary testing with cloud-based fleet analytics." CIRED - Open Access Proceedings Journal 2017, no. 1 (2017): 1367–69. http://dx.doi.org/10.1049/oap-cired.2017.0718.

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NARAYANAN, G. B. LAKSHMI. "Evaluating the Cost-Efficiency and Financial Sustainability of Cloud-Based ATE Solutions in Testing Environments." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31437.

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The introduction of cloud-based solutions in testing environments, especially in Automated Test Equipment (ATE), is bringing a big change in the way organizations approach testing processes. This change is driven by the potential for enhanced cost-efficiency and financial sustainability. As testing requirements continue to evolve, the scalability, flexibility, and accessibility offered by cloud-based ATE solutions present promising opportunities for optimizing resource utilization and reducing operational costs. This study aims to evaluate the economic feasibility of adopting cloud-based ATE solutions. It explores their impact on both short-term and long-term financial sustainability. By examining factors such as upfront investment, operational expenses, and the overall return on investment, this research seeks to provide insights into the practical implications of transitioning to cloud-based ATE solutions for organizations seeking to streamline their testing processes.

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Amaral, Janete, Alberto S. Lima, José Neuman de Souza, and Lincoln S. Rocha. "From the Art of Software Testing to Test-as-a-Service in Cloud Computing." International Journal of Software Engineering & Applications 13, no. 6 (2022): 01–21. http://dx.doi.org/10.5121/ijsea.2022.13601.

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Researchers consider that the first edition of the book "The Art of Software Testing" by Myers (1979) initiated research in Software Testing. Since then, software testing has gone through evolutions that have driven standards and tools. This evolution has accompanied the complexity and variety of software deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better return on investment. Cloud computing requires more significant involvement in software testing to ensure that services work as expected. In addition to testing cloud applications, cloud computing has paved the way for testing in the Test-as-a-Service model. This review aims to understand software testing in the context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of software testing, characterizing fundamental points and allowing us to compose a synthesis of the body of knowledge in software testing, expanded by the cloud computing paradigm.

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Janete, Amaral, S. Lima Alberto, Neuman de Souza José, and S. Rocha Lincoln. "FROM THE ART OF SOFTWARE TESTING TO TEST-AS-A-SERVICE IN CLOUD COMPUTING." International Journal of Software Engineering & Applications (IJSEA) 13, no. 6 (2023): 1–21. https://doi.org/10.5281/zenodo.7929320.

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Researchers consider that the first edition of the book "The Art of Software Testing" by Myers (1979) initiated research in Software Testing. Since then, software testing has gone through evolutions that have driven standards and tools. This evolution has accompanied the complexity and variety of software deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better return on investment. Cloud computing requires more significant involvement in software testing to ensure that services work as expected. In addition to testing cloud applications, cloud computing has paved the way for testing in the Test-as-a-Service model. This review aims to understand software testing in the context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of software testing, characterizing fundamental points and allowing us to compose a synthesis of the body of knowledge in software testing, expanded by the cloud computing paradigm.

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Janete, Amaral, S. Lima Alberto, Neuman de Souza José, and S. Rocha Lincoln. "From the Art of Software Testing to Test-as-a-Service in Cloud Computing." International Journal of Software Engineering & Applications (IJSEA) 13, no. 6 (2022): 1–21. https://doi.org/10.5281/zenodo.7376825.

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Researchers consider that the first edition of the book "The Art of Software Testing" by Myers (1979) initiated research in Software Testing. Since then, software testing has gone through evolutions that have driven standards and tools. This evolution has accompanied the complexity and variety of software deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better return on investment. Cloud computing requires more significant involvement in software testing to ensure that services work as expected. In addition to testing cloud applications, cloud computing has paved the way for testing in the Test-as-a-Service model. This review aims to understand software testing in the context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of software testing, characterizing fundamental points and allowing us to compose a synthesis of the body of knowledge in software testing, expanded by the cloud computing paradigm.

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Bediroğlu, G., and H. E. Colak. "CLOUD GIS BASED WATERSHED MANAGEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W6 (November 13, 2017): 31–33. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w6-31-2017.

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In this study, we generated a Cloud GIS based watershed management system with using Cloud Computing architecture. Cloud GIS is used as SAAS (Software as a Service) and DAAS (Data as a Service). We applied GIS analysis on cloud in terms of testing SAAS and deployed GIS datasets on cloud in terms of DAAS. We used Hybrid cloud computing model in manner of using ready web based mapping services hosted on cloud (World Topology, Satellite Imageries). We uploaded to system after creating geodatabases including Hydrology (Rivers, Lakes), Soil Maps, Climate Maps, Rain Maps, Geology and Land Use. Watershed of study area has been determined on cloud using ready-hosted topology maps. After uploading all the datasets to systems, we have applied various GIS analysis and queries. Results shown that Cloud GIS technology brings velocity and efficiency for watershed management studies. Besides this, system can be easily implemented for similar land analysis and management studies.

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SKORIN, Yuriy, Irynа ZOLOTАRYOVА, and Yuriy LYSTOPAD. "MANAGEMENT OF SCALABILITY IN CLOUD-BASED APPLICATIONS." Computer systems and information technologies, no. 3 (September 26, 2024): 58–66. http://dx.doi.org/10.31891/csit-2024-3-8.

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The following is an abstract of the article. The article presents an analysis of the challenges associated with monitoring and managing the scalability of a cloud application. To this end, a module for monitoring and managing the scalability of a cloud application has been developed as part of this study. The development process included the introduction of automatic scaling, and monitoring using Prometheus and Grafana, which allows for a high level of availability and resource efficiency. The study comprised a series of phases, including requirements analysis, system design, development, testing, and evaluation. Consequently, the system's performance, stability, and capacity to scale in response to fluctuating workloads were enhanced. The module exhibits a high degree of adaptability to changes in system requirements and load, which is a crucial attribute for the dynamic development of business applications. This solution assists in optimizing the allocation of resources and reducing infrastructure costs. The project has been found to fully meet the set goals and objectives, as well as the requirements for effective resource management of the Amazon Web Services cloud platform using Terraform, Prometheus, and Grafana. The practical value of the developed module is evidenced by a significant improvement in resource efficiency, service stability and cost optimisation. The module design has been subjected to rigorous testing and has been successfully implemented in a test environment, thereby demonstrating the sustainability and efficiency of the developed solution. The experience gained in the implementation and operation of this solution may prove useful for further expansion and optimization of cloud solutions in other projects and companies specializing in the provision of cloud solutions. The findings of this study were validated in a test environment at an IT company with a specialization in cloud technologies. The objective was to ascertain the functionality and efficiency of the developed module in a real-world context of cloud infrastructure operation. The testing process entailed the configuration of the module on pre-existing cloud infrastructure systems, its integration with Prometheus and Grafana for monitoring purposes, and the execution of a series of stress tests designed to assess the module's scalability. As a result of this testing, a number of critical points were identified that required further optimization. The results of the study and the issues identified during the project testing have enabled the identification of several areas for further improvement and development of the system. First and foremost, the optimization of automatic scaling algorithms represents a crucial avenue for improvement. The development of these algorithms should be oriented towards utilizing historical monitoring data to anticipate potential shifts in system load. Another pivotal area for enhancement is the precision of monitoring systems. The integration of supplementary tools and the expansion of existing monitoring systems' functionality will facilitate the acquisition of more comprehensive insights into the system's condition. This, in turn, will facilitate the expedient identification and eradication of potential issues.

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Kumar, Sarvesh, Dyagala Naga Sudha, Anupama Anupama, B. Kannadasan, Ajay Singh Yadav, and Dinesh Goyal. "Efficient complexity based adaptive system for cloud resources." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 383–92. http://dx.doi.org/10.47974/jim-1669.

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Cloud computing is a new IT concept, that is no longer solely applicable to the economic system however as nicely very beneficial in science. The issue of asset distribution and income expansion is likewise similarly significant, particularly about cloud security. This achieves the need of various displaying strategies including however not restricted, to security danger, asset assignment, and income boost models. These offerings are billed on a utilization basis. The cloud services are provided by the CSPs to the end users in an optimized way by using our mathematical proposed algorithm. This proposed algorithm is simulated in the cloud simulator. It prompts financially savvy arrangements by lessening the execution season of enormous application testing. As a piece of framework assets, cloud testing can accomplish its productivity by dealing with the boundaries like organization traffic, Circle Stockpiling, and Smash speed. In this paper, we propose another fluffy numerical model to accomplish a superior degree for the above boundaries. The results of the outcomes informs that the proposed algorithm CROS (Cloud Resource Optimized System) performances are better.

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Chen, Kai, Hong Tan, Jie Gao, and Yang Lu. "Big Data Based Design of Food Safety Cloud Platform." Applied Mechanics and Materials 536-537 (April 2014): 583–87. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.583.

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Food safety has been in the spotlight of the global attention. In 2013, Chinas food safety supervision policy was undertaken a major reform. China Food and Drug Administration (CFDA) takes over the responsibility of food safety management, which in the past was conducted by different government sectors. Under this new management system, how to carry out the practical work has become a new issue. This paper introduces an innovative food safety management mode adopted by Guizhou province. On the basis of latest information technology, food production enterprises, government, testing organizations and consumers are integrated into a unified food safety information service cloud platform. The core technology of cloud platform is composed of food safety knowledge system, testing management system, food safety information publicity system as well as mobile application. The food factory inspection data, government inspection data, testing organizations testing data and consumers purchasing information are integrated into food safety and nutrient test big data. Utilizing the data to explore the information that is needed by all the parties, this paper tries to provide a solution to the risk exchange problem faced by Chinas food safety issue. At the same time, food safety problem can be solved through the contribution of different stakeholders.

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Pineng, Martina, Eko Suripto Pasinggi, Lantana Dioren Rumpa, and Exzelen Tri Suharpania. "Prediksi Kenaikan Awan Di Wisata Lolai Berbasis Machine Learning." Jurnal Mosfet 4, no. 1 (2024): 01–11. http://dx.doi.org/10.31850/jmosfet.v4i1.2907.

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The increase in cloud cover is an important indicator in predicting upcoming weather. However, manual observations of cloud cover are still limited and time-consuming. Therefore, this research aims to develop a cloud cover classification model based on measurement data in Lolai using the Naive Bayes machine learning method. In this study, data on cloud cover, temperature, and humidity measurements were collected directly in Lolai for 30 days and using online BMKG data. Then, the data was processed and divided into training and testing datasets. The Naive Bayes model was applied to the training data and its accuracy was tested on the testing data. The research results show that the cloud cover classification model based on Naive Bayes has varying accuracy levels depending on the data source. For direct measurement data, the model achieved an accuracy rate of 63%, while for online BMKG data, the model achieved an accuracy rate of 80%. In testing on the testing data, the model successfully classified cloud cover based on temperature and humidity data. This research contributes to identifying the relationship between temperature, humidity, and cloud conditions and evaluates the performance of the Naive Bayes model in determining the influence of air temperature and humidity on cloud conditions. It is expected that this research can serve as a basis for the development of weather prediction systems in the future.

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Ya’u, Badamasi Imam, Norsaremah Salleh, Azlin Nordin, Ali Amer Alwan, Norbik Bashah Idris, and Hafiza Abas. "A SYSTEMATIC MAPPING STUDY ON CLOUD-BASED MOBILE APPLICATION TESTING." Journal of Information and Communication Technology 18, no. 4 (2019): 485–527. http://dx.doi.org/10.32890/jict2019.18.4.5.

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Hengliang, Shi, Zhao Changwei, Huang Tao, and Dong Yongsheng. "Research on Distributed Software Testing Platform Based on Cloud Resource." International Journal of Computer Science & Engineering Survey 4, no. 2 (2013): 17–25. http://dx.doi.org/10.5121/ijcses.2013.4202.

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Tao, Chuanqi, and Jerry Gao. "On building a cloud-based mobile testing infrastructure service system." Journal of Systems and Software 124 (February 2017): 39–55. http://dx.doi.org/10.1016/j.jss.2016.11.016.

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Tao, Dan, Zhaowen Lin, and Cheng Lu. "Cloud platform based automated security testing system for mobile internet." Tsinghua Science and Technology 20, no. 6 (2015): 537–44. http://dx.doi.org/10.1109/tst.2015.7349926.

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Purbaningtyas, Rani, Moh Munih Dian Widianta, and Mochammad Rifki Ulil Albaab. "Development of MetaPolije : Cloud-Based Metabase GIS Data Analysis Platform." International Journal of Technology, Food and Agriculture 1, no. 2 (2024): 63–71. http://dx.doi.org/10.25047/tefa.v1i2.4633.

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This study aims to discuss the successful development of MetaPolije. MetaPolije is a platform used for cloud-based spatial-text data analysis utilizing the metabase library. MetaPolije as an alternative solution for users who need a medium for textual data analysis while also displaying precise location data. This platform is developed following the stages of agile method, which consists of several stages, from system requirement to testing. The platform has also undergone functional, validity, and performance testing techniques to ensure its efficiency. The test results have shown that the platform is capable of functioning well according to its intended purpose. Additionally, the platform is able to accurately display visualized data analysis results, including precise location points for each datum. Therefore, it can be concluded that the MetaPolije is a viable platform that meets the standards of functional, validity, and performance testing, making it a dependable and effective tool for data analysis. MetaPolije has been successfully developed by providing the basic features needed by users when conducting spatial text data analysis. The test results indicate that MetaPolije has met the quality testing standards for software based on each specified testing indicator, covering functional, validity, and performance aspects. These features can also be customized according to the users' needs and purposes.

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Gollapudi, Pavan Kumar, and Rajkumar Govindaswamy Subbian. "Cloud Migrated Continuous Testing in DevOps: A Game-Changer for P&C Insurers." Asian Journal of Research in Computer Science 18, no. 3 (2025): 239–49. https://doi.org/10.9734/ajrcos/2025/v18i3590.

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Aim: The study examines Continuous Testing (CT) in a DevOps environment for cloud migration within the Property & Casualty (P&C) insurance industry and InsurTech companies. The study evaluates the impact of AI/ML-driven test automation, security testing, and performance validation using tools like Selenium like Selenium, JUnit, and TestNG; CI/CD pipelines such as Jenkins, GitHub Actions, and Azure DevOps. Experimental testing with comparative evaluations shows that a CT structured approach simplifies any cloud migration project and improves defect propagation and compliance ratios in regulated industries. Industry and Scientific Application: Beyond insurance, this study applies to other industries and scientific research. Continuous Testing drives innovation by detecting real-time defects, reducing deployment risks, and ensuring regulatory compliance. These findings can serve as a model for organizations integrating DevOps-driven testing in cloud migration. Benefits of Cloud Migration: CT in DevOps optimizes cloud migration by automating testing, reducing errors, and improving deployment efficiency. Companies using CT see 40-60% faster deployments and 35% fewer defects post-implementation. Automated security checks enhance compliance, while test automation lowers costs. These benefits make CT essential for a smooth, secure, and cost-effective cloud transition, especially in regulated sectors like finance, healthcare, and insurance. Case Studies and Real-World Application: Case studies of Liberty Mutual and Progressive Insurance have shown that Continuous Testing is effective and accelerates DevOps-centered cloud migration. Liberty Mutual used cloud-embedded automated test frameworks to reduce the release time period by 50% and achieve compliance, thereby cutting time-to-market. Progressive Insurance streamlined the testing of APIs and mobile applications, using CI/CD-integrated testing automation to produce faster claims processing at the rate of up to 30% and a drop of around 90% in API failures. Here, in these case studies, one sees how Continuous Testing massively contributes to deployment efficiency, system resilience, and adherence to regulations in real world case applications in insurance. Study Design: "This study takes a mixed methods approach that incorporates case studies, industry surveys, and experimental testing to assess the efficiency of Continuous Testing in cloud-migration strategies," instead. Its research targets insurance companies which have recently adopted DevOps-driven cloud migration and investigate their testing frameworks. Place and Duration of Study: This study is based on a review of industry practices, integration strategies and analysis of cloud migration strategies in global insurance firms across various companies in North America and Asia-Pacific, focusing on solutions implemented between 2018 and 2024. Methodology: The study employs a multiple research method approach, including reviews of the literature, case studies, surveys, and experimentation, to assess the impact of continuous testing (CT) for the DevOps-driven cloud migration of P&C insurers. Following a detailed literature review about the extant state of the research into CT and cloud adoption in insurance, case studies are available to demonstrate insurance URLs using CT-based frameworks. Surveys and interviews with IT and DevOps in-house experts underline the challenges and good practices. Through experimentation with automated testing tools such as Selenium, JUnit, and Jenkins, we measure the improvements in efficiency. A comparative analysis will measure the performance indicators prior to and after the CT implementation. Results: Continuous testing (CT) substantially enhances cloud-migration efficiency for P&C insurance. Companies that have had CT in their version of DevOps have executed a 40-60% increase in software release cycles, leading to faster deployments. Automated testing dragged post-deployment issues down by 35%, thereby increasing the reliability of the software. Compliance with industry requirements was much better because continuous security checks lessen risks. Another benefit included a reduction of about 20-30% in testing costs due to automation that replaced human testing. On top of this, the way for more applications to be resilient to system failures was opened; applications were supported and maintained. The post-migration data should always specify that applications have 99.9% up time. In the heavily regulated insurance sector, continuous testing thus becomes a much faster, more secure, and cost-effective measure for moving to the cloud. Conclusion: Continuous Testing in DevOps significantly enhances cloud migration for P&C insurers by improving speed-to-market, quality, and compliance. According to this discussion, automation is the key enabler for cloud adoption, which in turn mitigates risk and improves operational agility. One could advocate that P&C insurers' ability to pursue a CT division in the cloud epoch is tantamount to ensuring the unremitting progress of digital transformation.

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Ravichandran, S., and M. Umamaheswari. "Innovative Method of Software Testing Environment in Cloud Computing Technology." Asian Journal of Computer Science and Technology 3, no. 2 (2014): 34–39. http://dx.doi.org/10.51983/ajcst-2014.3.2.1738.

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Various information systems are widely used in information society era, and the demand for highly dependable system is increasing year after year. However, software testing for such a system becomes more difficult due to the enlargement and the complexity of the system. In particular, it is too difficult to test parallel and distributed systems sufficiently although dependable systems such as high-availability servers usually form parallel and distributed systems. To solve these problems, to propose a software testing environment for dependable parallel and distributed system using the cloud computing technology, named D-Cloud. D-Cloud includes Eucalyptus as the cloud management software, and FaultVM based on QEMU as the virtualization software, and D-Cloud frontend for interpreting test scenario. D-Cloud enables not only to automate the system configuration and the test procedure but also to perform a number of test cases simultaneously, and to emulate hardware faults flexibly. In this paper, present the concept and design of D-Cloud, and describe how to specify the system configuration and the test scenario. Furthermore, the preliminary test example as the software testing using D-Cloud was presented. Its result shows that D-Cloud allows to set up the environment easily, and to test the software testing for the distributed system.

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Wang, Guoshuai, Tonghui Xu, Decheng Wang, et al. "Cloud-Based Remote Real-Time Monitoring and Control System for Spring Fatigue Testing Machine." Machines 12, no. 7 (2024): 462. http://dx.doi.org/10.3390/machines12070462.

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In recent years, the utilization of cloud technology has witnessed a surge, particularly in the domains of industrial automation and intelligent scenarios. However, the prevailing spring fatigue testing machine is still in the traditional single-machine working mode. In this mode, there are many problems such as low automation of testing equipment, poor experimental site environment, and the need for experimenters to be on duty for a long time. In order to solve the above problems, this paper builds a cloud-based remote monitoring and control system based on the high-temperature constant-force spring fatigue testing machine. The system is based on Browser/Server architecture, and clients can access it anytime and anywhere using a browser in a public network environment. The server is hosted on a public cloud platform and includes website service, data storage service, WebSocket real-time communication service, and remote video monitoring service. Clients can remotely monitor and control the testing machine in real time through the cloud. After experimental verification, the real-time monitoring and control messages delay is 11 ms, and the video monitoring delay is 291 ms, which can meet the actual needs of remote spring fatigue testing. This remote monitoring and control system improves the automation of the spring fatigue testing machine and improves the working environment of the experimenters. In addition, it can be applied to other reliability testing machines in the laboratory, and can further help build a workshop-level remote monitoring and control platform.

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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 (ML) for workload forecasting and reinforcement learning (RL) for real-time resource adaptation.The proposed framework utilizes ML models such as Long Short-Term Memory (LSTM) and XGBoost to analyze historical workload patterns and predict future demand. In parallel, RL-based techniques, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), dynamically adjust resource allocation based on system performance in real time. Experimental evaluations conducted in a cloud-based test environment demonstrate that the AI-driven system outperforms traditional autoscaling methods, reducing resource adjustment time by 50%, improving workload prediction accuracy by 30-40%, and lowering cloud computing costs by 35-50%.Beyond performance gains, the AI-driven approach enhances service reliability, system responsiveness, and workload balancing by proactively preventing resource bottlenecks and overload conditions. However, challenges remain in handling unexpected workload spikes, minimizing computational overhead for AI inference, and adapting models to diverse application environments. Future research should explore collaborative AI-driven workload models for multi-cloud environments, interpretable AI techniques for transparent decision-making, and advanced computing methods for optimizing real-time AI-based workload adjustments.The findings of this study highlight the potential of AI-powered workload management in transforming cloud performance optimization. By enabling self-adjusting, intelligent cloud systems with minimal human intervention, this approach offers significant advantages for cloud service providers, SaaS companies, and enterprises aiming to enhance operational efficiency and cost-effectiveness.

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Bankert, Richard L., and Robert H. Wade. "Optimization of an Instance-Based GOES Cloud Classification Algorithm." Journal of Applied Meteorology and Climatology 46, no. 1 (2007): 36–49. http://dx.doi.org/10.1175/jam2451.1.

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Abstract An instance-based nearest-neighbor algorithm was developed for a Geostationary Operational Environmental Satellite (GOES) cloud classifier. Expert-labeled samples serve as the training sets for the various GOES image classification scenes. The initial implementation of the classifier using the complete set of available training samples has proven to be an inefficient method for real-time image classifications, requiring long computational run times and significant computer resources. A variety of training-set reduction methods were examined to find smaller training sets that provide quicker classifier run times with minimal reduction in classifier testing set accuracy. General differences within real-time image classifications as a result of using the various reduction methods were also analyzed. The fast condensed nearest-neighbor (FCNN) method reduced the size of the individual training sets by 68.3% (fourfold cross-validation testing average) while the average overall accuracy of the testing sets decreased by only 4.1%. Training sets resulting from these reduction methods were also applied within a real-time classifier using a one-nearest-neighbor subroutine. Using the FCNN-reduced set, the subroutine run time on a 30° latitude × 30° longitude image (GOES-10 daytime) with 11 289 600 total pixels decreased by an average of 60.7%.

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Koneru, Sri Harsha. "Cloud-Based Digital Twins: Revolutionizing Endpoint Infrastructure Management." European Journal of Computer Science and Information Technology 13, no. 23 (2025): 96–114. https://doi.org/10.37745/ejcsit.2013/vol13n2396114.

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This article explores the emerging paradigm of cloud-based digital twins for endpoint infrastructure simulation, which represents a significant advancement in enterprise IT management. In today's complex enterprise environments characterized by distributed workforces and diverse device ecosystems, organizations face mounting challenges in managing endpoint infrastructure securely and efficiently. Digital twins—virtual replicas of physical endpoint environments—enable IT teams to conduct comprehensive testing of updates, security controls, and configuration changes before deployment to production systems. The article examines the technical architecture underpinning these systems, including data collection mechanisms, simulation engines, orchestration layers, analytics frameworks, and recommendation systems. It details the structured workflow through which organizations can systematically evaluate proposed changes, from initial environment modeling through to deployment strategy development. Current implementations demonstrate compelling value across multiple use cases, including software update testing, ransomware response simulation, and compliance policy optimization. Beyond technical capabilities, digital twins deliver substantial business value through risk reduction, accelerated deployment cycles, resource optimization, and improved security postures. The article concludes by exploring future directions, including integration with DevOps pipelines, expanded behavioral modeling, and cross-environment simulation.

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Bipin Gajbhiye, Shalu Jain, and Pandi Kirupa Gopalakrishna Pandian. "Penetration Testing Methodologies for Serverless Cloud Architectures." Innovative Research Thoughts 8, no. 4 (2022): 347–59. http://dx.doi.org/10.36676/irt.v8.i4.1456.

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As organizations increasingly adopt serverless cloud architectures to enhance scalability and reduce operational costs, the security landscape has evolved, introducing new challenges and vulnerabilities. Serverless computing, characterized by its abstraction of infrastructure management and dynamic resource allocation, presents unique security concerns that traditional penetration testing methodologies may not adequately address. This research paper explores penetration testing methodologies specifically tailored for serverless cloud environments, aiming to identify effective strategies for evaluating and mitigating security risks in these modern architectures. The paper begins by defining serverless computing and its key characteristics, such as event-driven execution, automatic scaling, and micro-billing models. Unlike traditional server-based environments, serverless architectures often rely on Functions-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) components, which can obscure the underlying infrastructure and introduce complex attack vectors. Consequently, traditional penetration testing approaches, designed for monolithic or microservices-based systems, may fall short in identifying and exploiting vulnerabilities specific to serverless environments.

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Manuja, Bandal. "AI-Enabled Adaptive Fault Injection for Self-Regulating Software Testing in AWS Cloud Platforms." Journal of Scientific and Engineering Research 9, no. 12 (2022): 236–45. https://doi.org/10.5281/zenodo.15044761.

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Testing software in cloud environments, especially within AWS infrastructures, presents distinct obstacles due to dynamic resource allocation, decentralized architectures, and unpredictable execution conditions. Conventional testing methods often fail to detect cloud-specific faults such as transient errors, race conditions in autoscaling, and inconsistencies in distributed systems. This paper introduces AI-Enabled Adaptive Fault Injection (AIAFI), a novel autonomous testing framework specifically designed for AWS-based applications. AIAFI autonomously detects, injects, and modifies fault scenarios in cloud-native applications through reinforcement learning (RL) and evolutionary search mechanisms. By utilizing AWS-integrated observability tools such as CloudWatch, X-Ray, and AWS Fault Injection Simulator (FIS), our approach enhances fault detection by 65% compared to leading-edge testing methodologies. Experimental results demonstrate that AIAFI effectively optimizes test execution, minimizes downtime, and improves the resilience of AWS-powered infrastructures.

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Journal articles on the topic 'Cloud-Based Testing'

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