大数据应用质量保证技术的系统研究综述
Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review
摘要：
大数据应用程序目前用于许多应用领域，从统计应用到预测系统和智慧城市。然而，这些应用程序的质量远非完美，导致了大量的问题和问题。因此，确保大数据应用的整体质量发挥着越来越重要的作用。本文旨在总结和评估现有的质量保证（QA）技术，解决大数据应用中的质量问题。我们通过检索主要科学数据库进行了系统性文献综述（SLR），获得了83项关于大数据应用QA技术的主要和相关研究。SLR结果揭示了以下主要发现：1）大数据的数量、速度和多样性属性对大数据应用质量的影响; 2）决定大数据应用质量的质量属性包括正确性、性能、可用性、可扩展性、可靠性等; 3）现有的QA技术，包括分析、规格说明、模型驱动架构（MDA）、验证、容错、测试、监控和故障与失效预测; 4）每种QA技术的现有优势和局限性; 5）每种QA技术现有的经验证据。该研究为大数据应用的质量保证技术研究提供了坚实的基础。然而，大数据应用在质量方面的许多挑战仍然存在。
英文摘要：
Big data applications are currently used in many application domains, ranging from statistical applications to prediction systems and smart cities. However, the quality of these applications is far from perfect, leading to a large amount of issues and problems. Consequently, assuring the overall quality for big data applications plays an increasingly important role. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. The SLR results reveal the following main findings: 1) the impact of the big data attributes of volume, velocity, and variety on the quality of big data applications; 2) the quality attributes that determine the quality for big data applications include correctness, performance, availability, scalability, reliability and so on; 3) the existing QA technologies, including analysis, specification, model-driven architecture (MDA), verification, fault tolerance, testing, monitoring and fault & failure prediction; 4) existing strengths and limitations of each kind of QA technology; 5) the existing empirical evidence of each QA technology. This study provides a solid foundation for research on QA technologies of big data applications. However, many challenges of big data applications regarding quality still remain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
