Automated Software Testing Frameworks for Cloud-Native Applications
DOI:
https://doi.org/10.15662/IJARCST.2022.0504001Keywords:
Automated Testing, Cloud-Native Applications, Microservices, EVOMASTER, EvoSuite, Contract Testing (Pact), BDD (Cucumber, Karate), Integration Testing (Postman, WireMock)Abstract
Cloud-native applications—commonly composed of microservices and deployed in containerized, dynamic environments—demand robust, automated testing frameworks to ensure reliability, scalability, and rapid deployment. This paper surveys key automated testing tools and methodologies established before 2019 for cloud native systems. We analyze techniques including white-box RESTful API test-case generation (EVOMASTER), automated unit test generation (EvoSuite), BDD frameworks (Cucumber), API contract testing (Pact), and integration testing tools such as Postman, Karate, and Wiremock. Through literature synthesis and selected case studies, we assess how these frameworks address the unique complexities of microservices—such as frequent deployment, service contracts, and test environment instability. The methodology blends systematic literature review with practical evaluation via implementation scenarios. Notable findings include the effectiveness of consumer-driven contract testing and mocking strategies to decouple services during testing, and the use of automated test generation tools to discover edge-case faults in REST APIs and Java code. We propose a structured testing workflow for cloud-native applications: service decomposition analysis, unit and mutation test generation, contract definition and testing, API and integration testing with simulation of dependencies, and CI-linked test automation. Advantages include improved test coverage, faster feedback loops, and reduced reliance on fragile end-to-end tests; disadvantages involve higher complexity in setup, potential maintenance overhead, and the challenge of generating meaningful tests in distributed environments. Results show that combining these tools within CI/CD pipelines enhances reliability and scalability. We conclude that automated testing frameworks are essential to cloud-native development; future work should explore AI-assisted test generation, better orchestration of ephemeral test environments, and integrated observability-driven testing.
References
1. Arcuri, A. (2019). RESTful API Automated Test Case Generation (EVOMASTER). arXiv preprint (turn0academia14).
2. Fraser, G., & Arcuri, A. (2013–2015). EvoSuite—Search-based Java unit test generation. Various conferences/journals (turn0search16).
3. Cloud-Native Testing Perspectives (DevOps testing practices). Medium (turn0search1).
4. Comprehensive Microservices Testing Frameworks. MDPI Mapping Study (turn0search3).
5. Microservices Testing Case Study (UK media). InfoQ (turn0search7).
6. Automating Integration and API Testing Tools. Cloud Native Blogs (turn0search0).
7. Evolution of Testing in Microservices (Netflix practices). 2i Testing Blog.
8. Santhoshini, G., & Anbazhagan, K. (2014, February). An object based software tool for software measurement. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-5). IEEE.
9. Gentyala, R. (2021). Bridging the Semantic Gap: A Lightweight Ontological Framework for Real-Time Harmonization of Consumer Wearable Data with FHIR-Based EHR Systems. IACSE-International Journal of Computer Technology (IACSE-IJCT), 2(1), 24-77.
10. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
11. Deivendran, P., Anbazhagan, K., Sailaja, P., Sujatha, E., Babu, M. R., & Sudhakar, S. (2020). Scalability service in data center persistent storage allocation using virtual machines. International Journal of Scientific & Technology Research, 9(02), 2135-2139.
12. Padala, S. (2021). Cloud-Enabled AI Contact Centers in Oncology Care. International Journal of AI, BigData, Computational and Management Studies, 2(3), 93-98.
13. Murugeshwari, B., & Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
14. Pushparathi, V. G., Sudha, M., David, D. J., Anbazhagan, K., & Vethamani, S. E. (2020). A Continuous Decision Based Multi Kernel Median Filter for Noise Removal on Brain MRI Images. Advanced imaging, 1(3), 5.
15. Watham, S. D., & Vimal, V. R. (2013). Design and Implementation of Data Sanitization Technique For Effective Filtering With Enhanced Medical Support System in Cloud Architecture Diagram. International Journal of Emerging Technology and Advanced Engineering, 3(12), 471-473.
16. Kumar, J. (2013). Preservation of the Privacy for Multiple Custodian Systems with Rule Sharing. Journal of Computer Science.
17. Sheta, S.V. (2022). An Overview of Object-Oriented Programming (OOP) and Its Impact on Software Design. Educational Administration: Theory and Practice, 28(4), 409–419.
18. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
19. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
20. Rajasekharan, R. (2017). The role of DevOps automation in improving enterprise database reliability. International Journal of Humanities and Information Technology (IJHIT), 2(1), 20–29.
21. Potel, R. (2021). A Data-Driven Architecture for Preemptive Cyber Defense Using AI-Based Governance and Autonomous Remediation. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(6).
22. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
23. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
24. Katta, T. B. (2022). Cloud-native integration frameworks for modern enterprises: Driving scalable and resilient digital transformation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(3), 4926–4938.
25. Murugeshwari, B., Amirthavalli, R., Sri, C. B., & Pari, S. N. (2023). Hybrid key authentication scheme for privacy over adhoc communication. arXiv preprint arXiv:2304.14652.
26. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
27. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
28. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
