2026-06-29T08:19:37Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/1169592025-01-30T01:15:27Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Cerro Cañizares, Pablo author Núñez Covarrubias, Alberto author García Merayo, María De Las Mercedes 2025-01-29T15:07:37Z 2025-01-29T15:07:37Z 2016-06-06 10.1016/J.PROCS.2016.05.298 https://hdl.handle.net/20.500.14352/116959 https://doi.org/10.1016/J.PROCS.2016.05.298 During the last decade, the fast evolution in communication networks has facilitated the development of complex applications that manage vast amounts of data, like Big Data applications. Unfortunately, the high complexity of these applications hampers the testing process. Moreover, generating adequate test suites to properly check these applications is a challenging task due to the elevated number of potential test cases. Mutation testing is a valuable technique to measure the quality of the selected test suite that can be used to overcome this difficulty. However, one of the main drawbacks of mutation testing lies on the high computational cost associated to this process. In this paper we propose a dynamic distributed algorithm focused on HPC systems, called EMINENT, which has been designed to face the performance problems in mutation testing techniques. EMINENT alleviates the computational cost associated with this technique since it exploits parallelism in cluster systems to reduce the final execution time. In addition, several experiments have been carried out on three applications in order to analyse the scalability and performance of EMINENT. The results show that EMINENT provides an increase in the speed-up in most scenarios. eng Attribution-NonCommercial-ShareAlike 4.0 International EMINENT: Embarrassingly parallel mutation testing conference paper 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URL https://docta.ucm.es/bitstreams/3b10f2f8-9043-4545-984f-955db77477b7/download File MD5 fd7a902339041279c4ffd524a6c5e45a 289342 application/pdf EMINENT.pdf