Big Data Testing Techniques:Taxonomy,Challenges and Future Trends
作者机构：SRITUSAthloneIreland Mälardalen UniversityVästeråsSweden
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2023年第74卷第2期
页 面：2739-2770页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：Science Foundation Ireland(SFI)under Grant Number SFI/16/RC/3918(Confirm)and Marie Sklodowska Curie Grant agreement No.847577 co-funded the European Regional Development Fund.Wasif Afzal has received funding from the European Union’s Horizon 2020 research and innovation program under CMC,2023,vol.74,no.22767 Grant agreement Nos.871319,957212 from the ECSEL Joint Undertaking(JU)under Grant agreement No 101007350
主 题：Big data testing techniques testing process
摘 要：Big Data is reforming many industrial domains by providing decision support through analyzing large data *** Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of ***,because of the diversity and complexity of data,testing Big Data is *** numerous research efforts deal with Big Data testing,a comprehensive review to address testing techniques and challenges of BigData is not available as ***,we have systematically reviewed the Big Data testing techniques’evidence occurring in the period 2010–*** paper discusses testing data processing by highlighting the techniques used in every processing ***,we discuss the challenges and future *** findings show that diverse functional,non-functional and combined(functional and non-functional)testing techniques have been used to solve specific problems related to Big *** the same time,most of the testing challenges have been faced during the MapReduce validation *** addition,the combinatorial testing technique is one of the most applied techniques in combination with other techniques(i.e.,random testing,mutation testing,input space partitioning and equivalence testing)to find various functional faults through Big Data testing.
维普期刊数据库