A Survey on Quality Assurance Techniques for Big Data Applications

With the rapid advance of big data and cloud computing, building high quality big data systems in different application fields has gradually became a popular research topic in academia and industry as well as government agencies. However, more quality problems lead to application errors. Although the current research work has discussed how to ensure the quality of big data applications from several aspects, there is no systematic discussion on how to ensure the quality of large data applications. Therefore, a systematic study on big data application quality assurance is very necessary and critical. This paper focuses on the survey of quality assurance techniques of big data applications, and it introduces big data properties and quality attributes. It mainly discusses the key approaches to ensure the quality of big data applications and they are testing, model-driven architecture (MDA), monitoring, fault tolerance, verification and also prediction techniques. In addition, this paper also discusses the impact of big data characteristics on big data applications.