Artificial Intelligence for Continuous Quality Assurance in Cloud-Native Applications: A Review
DOI:
https://doi.org/10.47672/ejt.2895Keywords:
Cloud-Native Applications (CNA), Continuous Quality Assurance (CQA), Artificial Intelligence (AI), Microservices, Containerization, Kubernetes, Smart Cloud EnvironmentsAbstract
Purpose: This research aims to explore the role of Artificial Intelligence (AI) in enhancing quality assurance (QA) processes within cloud-native applications (CNAs). Specifically, it investigates how AI can address the challenges posed by the increasing complexity of cloud-native ecosystems and their associated quality control issues.
Methodology: The study adopts a qualitative research approach, involving case studies of organizations implementing AI-driven QA tools in cloud-native environments. Data is collected through interviews with industry experts, analysis of existing AI QA tools, and a review of relevant academic and industry literature. The research focuses on identifying the key AI technologies used, their integration into existing QA processes, and the impact on QA efficiency and accuracy.
Findings: The integration of AI in QA processes within CNAs has shown promising results in automating test generation, defect prediction, anomaly detection, and incident response. Machine learning, deep learning, and transformer-based models have enabled advanced functions such as log analysis, future failure prediction, and real-time test script maintenance. These technologies have significantly improved test reliability, coverage, and efficiency, particularly in highly automated and distributed cloud environments.
Implications to Theory, Practice, and Policy: From a theoretical perspective, this research contributes to the understanding of how AI can reshape traditional QA methodologies in the context of cloud-native applications. It highlights the need for a shift from manual, static testing approaches to intelligent, adaptive, and continuous QA processes. Practically, the study provides insights for organizations looking to integrate AI into their QA workflows, offering a roadmap for implementation and potential pitfalls. Policymakers can use these findings to guide the development of standards and best practices for AI-driven QA in cloud-native applications, ensuring that performance, resiliency, and efficiency are maintained across distributed cloud environments.
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References
[1] D. Gannon, R. Barga, and N. Sundaresan, “Cloud-Native Applications,” IEEE Cloud Comput., vol. 4, pp. 16–21, 2017, doi: 10.1109/MCC.2017.4250939.
[2] N. Astyrakakis, Y. Nikoloudakis, I. Kefaloukos, C. Skianis, E. Pallis, and E. K. Markakis, “Cloud-Native Application Validation & Stress Testing through a Framework for Auto-Cluster Deployment,” in 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2019, pp. 1–5. doi: 10.1109/CAMAD.2019.8858164.
[3] J.-M. Fernandez, I. Vidal, and F. Valera, “Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms,” Sensors, vol. 19, no. 13, p. 2980, Jul. 2019, doi: 10.3390/s19132980.
[4] M. Imran, H. Hlavacs, I. U. Haq, B. Jan, F. A. Khan, and A. Ahmad, “Provenance based data integrity checking and verification in cloud environments,” PLoS One, vol. 12, no. 5, p. e0177576, May 2017, doi: 10.1371/journal.pone.0177576.
[5] M. Muniswamaiah, T. Agerwala, and C. Tappert, “Big Data in Cloud Computing Review and Opportunities,” Int. J. Comput. Sci. Inf. Technol., 2019, doi: 10.5121/ijcsit.2019.11404.
[6] M. Shahin, M. Ali Babar, and L. Zhu, “Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices,” 2017. doi: 10.1109/ACCESS.2017.2685629.
[7] L. A. Vayghan, M. A. Saied, M. Toeroe, and F. Khendek, “Kubernetes as an Availability Manager for Microservice Applications,” 2019.
[8] V. Medel, O. Rana, J. ángel Bañares, and U. Arronategui, “Modelling performance & resource management in kubernetes,” in Proceedings of the 9th International Conference on Utility and Cloud Computing, 2016, pp. 257–262. doi: 10.1145/2996890.3007869.
[9] A. Kushwaha, P. Pathak, and S. Gupta, “Review of optimize load balancing algorithms in cloud,” Int. J. Distrib. Cloud Comput., vol. 4, no. 2, pp. 1–9, 2016.
[10] M. Faheem, U. Akram, I. Khan, S. Naqeeb, A. Shahzad, and A. Ullah, “Cloud Computing Environment and Security Challenges: A Review,” Int. J. Adv. Comput. Sci. Appl., 2017, doi: 10.14569/ijacsa.2017.081025.
[11] Q. K. Kadhim, R. Yusof, H. S. Mahdi, S. S. Ali Al-shami, and S. R. Selamat, “A Review Study on Cloud Computing Issues,” J. Phys. Conf. Ser., vol. 1018, p. 012006, May 2018, doi: 10.1088/1742-6596/1018/1/012006.
[12] S. Achouche, U. B. Yalamanchi, and N. Raveendran, “Method, apparatus, and computer-readable medium for performing a data exchange on a data exchange framework,” 2019
[13] T. Hirota, K. Okuda, M. Masuda, and S. Yasukawa, “Cloud Native SDx Control Technology,” NTT Tech. Rev., vol. 16, no. 7, pp. 39–43, Jul. 2018, doi: 10.53829/ntr201807ra1.
[14] M. M. El Khatib, A. Al-Nakeeb, and G. Ahmed, “Integration of Cloud Computing with Artificial Intelligence and Its Impact on Telecom Sector—A Case Study,” iBusiness, vol. 11, no. 01, pp. 1–10, 2019, doi: 10.4236/ib.2019.111001.
[15] S. Garg, “Predictive Analytics and Auto Remediation using Artificial Inteligence and Machine learning in Cloud Computing Operations,” Int. J. Innov. Res. Eng. Multidiscip. Phys. Sci., vol. 7, no. 2, 2019.
[16] P. Srivastava and R. Khan, “A Review Paper on Cloud Computing,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., 2018, doi: 10.23956/ijarcsse.v8i6.711.
[17] B. H. Li, B. Hou, W. Yu, X. Lu, and C. Yang, “Applications of artificial intelligence in intelligent manufacturing: a review,” Front. Inf. Technol. Electron. Eng., vol. 18, no. 1, pp. 86–96, Jan. 2017, doi: 10.1631/FITEE.1601885.
[18] S. Gina, N. I. Belu, N. Rachieru, and V. Nicolae, “Improvement of the customer satisfaction through Quality Assurance Matrix and QC-Story methods: A case study from automotive industry,” IOP Conf. Ser. Mater. Sci. Eng., vol. 252, no. 1, p. 012045, Oct. 2017, doi: 10.1088/1757-899X/252/1/012045.
[19] R. von Solms and M. Willett, “Cloud computing assurance – a review of literature guidance,” Inf. Comput. Secur., vol. 25, no. 1, pp. 26–46, Mar. 2017, doi: 10.1108/ICS-09-2015-0037.
[20] N. Kumar, “Anomaly Detection in ERP Systems Using AI and Machine Learning,” Int. J. Sci. Res. Sci. Eng. Technol., pp. 522–530, 2019.
[21] R. Jathanna and D. Jagli, “Cloud Computing and Security Issues,” Int. J. Eng. Res. Appl., vol. 07, no. 06, pp. 31–38, Jun. 2017, doi: 10.9790/9622-0706053138.
[22] L. B. A. Rabai, M. Jouini, A. Ben Aissa, and A. Mili, “A cybersecurity model in cloud computing environments,” J. King Saud Univ. - Comput. Inf. Sci., vol. 25, no. 1, pp. 63–75, Jan. 2013, doi: 10.1016/j.jksuci.2012.06.002.
[23] J. A. D. C. A. Jayakody, A. K. A. Perera, and G. L. A. K. N. Perera, “Cloud Native Efficient Solution for API Migration Across Environments For Agile Integration Enterprises,” in 2019 International Conference on Advancements in Computing (ICAC), IEEE, Dec. 2019, pp. 168–173. doi: 10.1109/ICAC49085.2019.9103393.
[24] N. Kratzke and P. C. Quint, “Understanding cloud-native applications after 10 years of cloud computing - A systematic mapping study,” J. Syst. Softw., 2017, doi: 10.1016/j.jss.2017.01.001.
[25] S. C. Vankayala, “Engineering Elastic Performance Testing Frameworks for Cloud- Native Applications: A Scalable Design Perspective,” J. Sci. Eng. Res., vol. 5, no. 8, pp. 301–315, 2018.
[26] R. Mikkilineni, G. Morana, and S. Keshan, “Globally interoperable network of clouds and cognitive workload quality of service assurance,” in Proceedings - 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2017, 2017. doi: 10.1109/WETICE.2017.10.
[27] F. Oliveira et al., “Delivering software with agility and quality in a cloud environment,” IBM J. Res. Dev., vol. 60, no. 2–3, pp. 10:1-10:11, Mar. 2016, doi: 10.1147/JRD.2016.2517498.
Routhu, K. K. (2022). From Case Management to Conversational HR: Redefining Help Desks with Oracle’s AI and NLP Framework. International Journal of Science, Engineering and Technology, 10(6).
Attipalli, A., BITKURI, V., KURMA, J., Enokkaren, S., Kendyala, R., & Mamidala, J. V. (2021). A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions. Available at SSRN 5741342.
Attipalli, A., BITKURI, V., Mamidala, J. V., Kendyala, R., & KURMA, J. (2022). Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies. Available at SSRN 5741263.
Attipalli, A., Enokkaren, S., BITKURI, V., Kendyala, R., KURMA, J., & Mamidala, J. V. (2021). Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection. Available at SSRN 5741305.
Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, V., Enokkaren, S. J., & Attipalli, A. (2021). Systematic Review of Artificial Intelligence Techniques for Enhancing Financial Reporting and Regulatory Compliance. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 73-80.
Kothamaram, R. R., Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., & Maniar, V. (2021). A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll Management Systems in Across Organizations. International Journal of Emerging Research in Engineering and Technology, 2(2), 64-72.
Padur, S. K. R. (2018). Autonomous cloud economics: AI driven right sizing and cost optimization in hybrid infrastructures. International Journal of Scientific Research in Science and Technology, 4(5), 2090-2097.
Padur, S. K. R. (2019). Machine learning for predictive capacity planning: Evolution from analytical modeling to autonomous infrastructure. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 5(5), 285-293.
Padur, S. K. R. (2020). AI augmented disaster recovery simulations: From chaos engineering to autonomous resilience orchestration. International Journal of Scientific Research in Science, Engineering and Technology, 7(6), 367-378.
Padur, S. K. R. (2020). From centralized control to democratized insights: Migrating enterprise reporting from IBM Cognos to Microsoft Power BI. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 6(1), 218-225.
Padur, S. K. R. (2021). Bridging Human, System, and Cloud Integration through RESTful Automation and Governance. the International Journal of Science, Engineering and Technology, 9(6).
Padur, S. K. R. (2021). Deep learning and process mining for ERP anomaly detection: Toward predictive and self-monitoring enterprise platforms. Available at SSRN 5605531.
Padur, S. K. R. (2021). From Control to Code: Governance Models for Multi-Cloud ERP Modernization. International Journal of Scientific Research & Engineering Trends, 7(3).
Padur, S. K. R. (2022). AI augmented platform engineering, transforming developer experience through intelligent automation and self optimizing internal platforms. International Journal of Science, Engineering and Technology, 10(5), 10-5281.
Padur, S. K. R. (2022). Intelligent resource management: AI methods for predictive workload forecasting in cloud data centers. J. Artif. Intell. Mach. Learn. & Data Sci, 1(1), 2936-2941.
Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2022). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 31-41.
Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.
Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., Maniar, V., & Kothamaram, R. R. (2021). Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 83-91.
Reddy Padur, S. K. (2021). From Scripts to Platforms-as-Code: The Role of Terraform and Ansible in Declarative Infrastructure Rollouts. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 621-628.
Routhu, K. K. (2021). Harnessing AI Dashboards in Oracle Cloud HCM: Advancing Predictive Workforce Intelligence and Managerial Agility. International Journal of Scientific Research & Engineering Trends, 7(6).
Routhu, K. K. (2022). From RFID to Geofencing: IoT-Enabled Smart Time Tracking in Oracle HCM Cloud. International Journal of Science, Engineering and Technology, 10(4).
Singh, A. A. S., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Predictive Modeling for Classification of SMS Spam Using NLP and ML Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 60-69.
Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., & Patchipulusu, H. H. S. (2022). Blockchain Technology in Supply Chain and Logistics: A Comprehensive Review of Applications, Challenges, and Innovations. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 72-80.
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Copyright (c) 2023 Usha Mohani kavirayani, Krishna Bhardwaj Mylavarapu, Jenitha Pilli, Prathik Kumar Jannu, Javed Ali Mohammad, Sri Harsha Panchali
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