Uzun-Per, MeryemCan, Ali BurakGürel, Ahmet VolkanAktaş, Mehmet S.2022-01-182022-01-182021Uzun-Per, M., Can, A. B., Gürel, A. V., & Aktaş, M. S. (2021). Big data testing framework for recommendation systems in e-science and e-commerce domains. 2021 IEEE International Conference on Big Data, pp. 2353-2361. https://doi.org/10.1109/bigdata52589.2021.9672082https://doi.org/10.1109/bigdata52589.2021.9672082https://hdl.handle.net/20.500.13055/138Software testing is an important process to evaluate whether the developed software applications meet the required specifications. There is an emerging need for testing frameworks for big data software projects to ensure the quality of the big data applications and satisfy the user requirements. In this study, we propose a software testing framework that can be utilized in big data projects both in e-science and e-commerce. In particular, we design the proposed framework to test big data-based recommendation applications. To show the usability of the proposed framework, we provide a reference prototype implementation and use the prototype to test a big data recommendation application. We apply the prototype implementation to test both functional and non-functional methods of the recommendation application. The results indicate that the proposed testing framework is usable and efficient for testing the recommendation systems that use big data processing techniques.eninfo:eu-repo/semantics/closedAccessTesting FrameworkTesting for Big Data ProjectsEcommendation SystemsBig Data AlgorithmsDistributed SystemsSpark MLlibConference Object10.1109/bigdata52589.2021.967208223532361WOS:0008005595020592-s2.0-85125363741