Open Access
Survey of Artificial Intelligence-Driven Test Engineering for Secure Cloud Native Systems
4
Associate Professor, Department of CSE, AIKIET Group of Institutions, Delhi NCR Ghaziabad, India
Abstract
Cloud-native technologies, which include microservices, containers, Kubernetes, and serverless computing, have been growing rapidly in recent times and have revolutionized the world of software development. Although the emergence of these technologies is beneficial in making software more scalable, flexible, and deployable, there exist certain difficulties that come about with them in software testing and security. Traditional methods of testing have found themselves ill-equipped to deal with such complex and dynamic environments, where artificial intelligence emerges as an ideal solution. The survey aims to study the role of AI-enabled test engineering strategies in creating secure cloud-native systems. This includes investigating the use of Machine Learning, Deep Learning, Natural Language Processing, and Reinforcement Learning in planning, testing, and deploying software. This survey will focus on various AI-enabled strategies in automated test case creation, test data creation, defect detection, test priority analysis, and deployment. Security issues such as malware detection, unauthorized access, runtime security threats, and decentralized access control are some other aspects to be covered in this survey. Some of the advantages of using AI-driven strategies for testing include greater automation, accuracy, and quicker deployment. On the downside, there are certain challenges such as the need for high-quality data, explainability, and computational complexity.
Keywords
Test Automation,
AI-Driven Test Engineering,
Cloud-Native Systems
References
M. Amaral, J. Polo, D. Carrera, I. Mohomed, M. Unuvar, and M. Steinder, “Performance Evaluation of Microservices Architectures Using Containers,” in 2015 IEEE 14th International Symposium on Network Computing and Applications, IEEE, Sep. 2015, pp. 27–34. doi: 10.1109/NCA.2015.49.
H. N. Dholariya, “Regulatory-Grade Autonomous Data Modernization: The RAMA Framework for Compliance-Aware AI-Native Cloud Architectures,” J. Comput. Anal. Appl., vol. 35, no. 1, pp. 883–898, Jan, Jan. 2026, doi: 10.48047/jocaaa.2026.35.01.40.
J. B. Mehta, “An Autonomous Dependency And Failure-Propagation Modeling System For Security-Critical Distributed Enterprise Cloud Computing Environments,” 202641001713, 2026
G. Blinowski, A. Ojdowska, and A. Przybylek, “Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation,” IEEE Access, vol. 10, pp. 20357–20374, 2022, doi: 10.1109/ACCESS.2022.3152803.
S. S. R. Ahamed, “Studying the Feasibility and Importance of Software Testing: An Analysis,” vol. 1, no. 3, pp. 119–128, 2010.
R. Madhumita and D. Chandana, “Advancing Software Testing: Integrating AI, Machine Learning, and Emerging Technologies,” Int. J. Res. Eng. Sci. Manag., vol. 7, no. January, p. 9, 2025.
B. Krishnan, S. Perla, S. Maddela, and R. Lingam, “Adaptive Multi-Cloud Infrastructure for CRM Analytics: Real-Time ML and Data Sync with LLMs,” in 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN), Lonawala, Maharashtra, India: IEEE, Nov. 2025, pp. 1–8. doi: 10.1109/GCWCN66157.2025.11448404.
H. V. Pandhare, “Future of Software Test Automation Using AI/ML,” Int. J. Eng. Comput. Sci., vol. 14, no. 05, pp. 27159–27182, 2025, doi: 10.18535/ijecs.v14i05.5139.
H. Jiang, J. Nagra, and P. Ahammad, “SoK: Applying Machine Learning in Security - A Survey,” 2016.
M. R. C. Mukkolakkal, “InfraLLM: A Generic Large Language Model Framework for Production-Grade Microservice Auto-Scaling in Cloud Infrastructure,” Int. J. Sci. Res. Mod. Technol., vol. 4, no. 11, pp. 113–123, 2025, doi: 10.38124/ijsrmt.v4i11.1023.
J. Kurma, “A Review of Security , Compliance , and Governance Challenges in Cloud-Native Middleware and Enterprise Systems,” vol. 5, no. 1, 2022, doi: 10.15662/IJRAI.2022.0501003.
O. C. Oyeniran, A. O. Adewusi, A. G. Adeleke, L. A. Akwawa, and C. F. Azubuko, “Microservices architecture in cloud-native applications: Design patterns and scalability,” Comput. Sci. IT Res. J., vol. 2, no. 1, pp. 92–106, 2024, doi: 10.51594/csitrj.v5i9.1554.
Z. Shen et al., “X-Containers,” in Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, New York, NY, USA: ACM, Apr. 2019, pp. 121–135. doi: 10.1145/3297858.3304016.
P. Naayini and S. Kamatala, “Automating Infrastructure Platforms with Cloud, Kubernetes, and Site Reliability Engineering,” Int. J. Comput. Tech., vol. 8, no. 6, pp. 1–9, November, 2021.
H. B. Dama, “A Survey of MySQL Database Administration Techniques and Best Practices,” ESP J. Eng. Technol. Adv., vol. 6, no. 1, pp. 89–98, February, 2026.
N. Kodakandla, “Serverless Architectures: A Comparative Study of Performance, Scalability, and Cost in Cloud-native Applications,” Iconic Res. Eng. Journals, vol. 5, no. 2, pp. 136–150, 2021.
T. Shah, “Cloud-Based Data Warehousing for Marketing Agility: Lessons from FinTech Migrations to Snowflake and AWS,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 4, no. 4, March, pp. 642–652, 2024, doi: 10.48175/IJARSCT-16000B.
K. Subramaniam, “Cloud Network Security: A Survey on Threats, Existing Solutions and the Path Forward,” Sch. Comput. Augment. Intell. Arizona State Univ., pp. 1–7, 2022.
R. Keshava, S. K. Pandurangan, M. Sakthivanitha, S. G. S. Parmsivan, and R. Maruthi, “AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections,” in 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India: IEEE, 2025, pp. 1673–1680, September. doi: 10.1109/ICESC65114.2025.11212519.
B. Matthew, S. Gupta, and A. Sen, “Migrating Legacy MES System Data Containing BOM, Routing, and Serialization Records to a Cloud-Native Lakehouse,” 2024.
T. Arif, B. Jo, and J. H. Park, “A Comprehensive Survey of Privacy-Enhancing and Trust-Centric Cloud-Native Security Techniques Against Cyber Threats,” Sensors, vol. 25, no. 8, p. 2350, Apr. 2025, doi: 10.3390/s25082350.
H. Jin, Z. Li, D. Zou, and B. Yuan, “DSEOM: A Framework for Dynamic Security Evaluation and Optimization of MTD in Container-based Cloud,” IEEE Trans. Dependable Secur. Comput., pp. 1–1, 2019, doi: 10.1109/TDSC.2019.2916666.
C. Patel, “A Review of Multi-Channel CRM Strategies Using Big Data and Cloud Integration,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 8, no. 1, pp. 577–588, 2022.
T. Bui, S. Rao, M. Antikainen, and T. Aura, “XSS Vulnerabilities in Cloud-Application Add-Ons,” in Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, New York, NY, USA, NY, USA: ACM, Oct. 2020, pp. 610–621. doi: 10.1145/3320269.3384744.
K. Jander, L. Braubach, and A. Pokahr, “Defense-in-depth and Role Authentication for Microservice Systems,” Procedia Comput. Sci., vol. 130, pp. 456–463, 2018, doi: 10.1016/j.procs.2018.04.047.
O. Safaryan, E. Pinevich, E. Roshchina, L. Cherckesova, and N. Kolennikova, “Information system development for restricting access to software tool built on microservice architecture,” E3S Web Conf., vol. 224, p. 01041, Dec. 2020, doi: 10.1051/e3sconf/202022401041.
M. K. Shah, “Artificial Intelligence (AI)- Based Threat Detection Models for Privileged App Abuse in Modern Systems,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 6, no. 4, p. 11, 2026.
R. Lingam, S. Nagi, M. Chigurupati, and B. T. Myneni, “Resilient DevSecOps: Self-Healing Cloud-Native Systems via SRE-Driven AI Threat Detection and Response,” in 2026 International Conference on Smart Futuristic Technology, IEEE, Jan. 2026, pp. 1–6. doi: 10.1109/ICSFT66733.2026.11508049.
H. Sofian, N. A. M. Yunus, and R. Ahmad, “Systematic Mapping: Artificial Intelligence Techniques in Software Engineering,” IEEE Access, vol. 10, pp. 51021–51040, 2022, doi: 10.1109/ACCESS.2022.3174115.
S. Kumar, “Artificial Intelligence in Software Engineering: A Systematic Exploration of AI-Driven Development,” Int. J. Innov. Res. Sci. , Eng. Technol., vol. 13, no. 6, pp. 11903–11913, 2024, doi: 10.15680/IJIRSET.2024.1306220.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, Second. The MIT Press, 2018.
E. Strubell, A. Ganesh, and A. McCallum, “Energy and policy considerations for modern deep learning research,” AAAI 2020 - 34th AAAI Conf. Artif. Intell., no. 1, pp. 1393–13696, 2020, doi: 10.1609/aaai.v34i09.7123.
M. Cinque, D. Cotroneo, and A. Pecchia, “Event Logs for the Analysis of Software Failures: A Rule-Based Approach,” IEEE Trans. Softw. Eng., vol. 39, no. 6, pp. 806–821, Jun. 2013, doi: 10.1109/TSE.2012.67.
S. Kumar, “Artificial intelligence in software engineering: A systematic exploration of AI-driven development,” Int. J. Innov. Res. Sci. Eng. Technol., vol. 13, no. 6, pp. 11903–11909, 2024.
N. Dragoni et al., “Microservices: Yesterday, Today, and Tomorrow,” in Present and Ulterior Software Engineering, M. Mazzara and B. Meyer, Eds., Cham: Springer International Publishing, 2017, pp. 195–216. doi: 10.1007/978-3-319-67425-4_12.
S. Choi, J. Goo, E. Jeon, M. Yang, and M. Jang, “ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor,” 2025. doi: 10.48550/arXiv.2505.09142.
M. H. Moghadam, “Machine learning-assisted performance testing,” in Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, New York, NY, USA, NY, USA: ACM, Aug. 2019, pp. 1187–1189. doi: 10.1145/3338906.3342484.
K. Kamaraj, B. Lanitha, S. Karthic, P. N. Senthil Prakash, and R. Mahaveerakannan, “A Hybridized Artificial Neural Network for Automated Software Test Oracle,” Comput. Syst. Sci. Eng., vol. 45, no. 2, pp. 1837–1850, 2023, doi: 10.32604/csse.2023.029703.
J. Čegiň, “Machine learning-based test data generation for safety-critical software,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, New York, NY, USA: ACM, Nov. 2020, pp. 1678–1681. doi: 10.1145/3368089.3418538.
A. Muhammad, “AI-Driven Testing Automation: Harnessing Machine Learning for Intelligent Test Case Creation and Predictive Defect Analysis,” 2023.
J. B. Mehta, “AI-Driven Test Engineering For Cloud-Native Systems,” Int. J. Data Sci. IoT Manag. Syst., vol. 5, no. 1, Jan. 2026, doi: 10.64751/ijdim.2026.v5i1.297.
M. S. Sree, C. K. K. Reddy, K. Vaishnavi, and V. Harika, “AI-Powered Software Engineering for Cloud-Native Environments,” 2025, pp. 57–86. doi: 10.4018/979-8-3693-9356-7.ch003.
P. Kale, “Architecting Autonomous Testing Pipelines for Cloud-Native Systems Using AI-Driven Fault Injection and Predictive Analytics,” Int. J. AI, Big Data, Comput. Manag. Stud., vol. 6, pp. 207–216, 2025, doi: 10.63282/3050-9416.IJAIBDCMS-V6I1P121.
K. Jayabalan, S. Parmsivan, G. Sunkara, M. Parikh, P. Challa, and V. Nutalapati, “Innovative Framework for Secure and Scalable Web and Mobile Application Development in Fintech: A User- Centric and AI - Driven Approach,” in 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), IEEE, Nov. 2025, pp. 1499–1505. doi: 10.1109/ICUIS67429.2025.11380645.
S. C. Vankayala, “Intelligent Quality Assurance in Cloud-Native Systems: A Deep Learning and Reinforcement Learning Approach to Adaptive Test Coverage Optimization,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 11, no. 6, pp. 546–553, Nov. 2024, doi: 10.32628/IJSRSET2512555.
V. R. Panyala, “International Journal of Advanced Research in Education and Technology ( IJARETY ) AI-Augmented DevOps Frameworks for Accelerating Cloud-Native Platform Engineering at Scale,” vol. 10, no. 1, 2023, doi: 10.15680/IJARETY.2023.1001028.
J. K. Shah, “AI-driven resilience in cloud-native big data platforms against cyberattacks,” J. Comput. Sci. Technol. Stud., vol. 4, no. 2, pp. 191–199, 2022, doi: 10.32996/jcsts.2022.4.2.23.
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