# Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review
## 4 Results
### Identify the Focused Quality Attributes in Big Data Applications (RQ1)
The rapid growth of data has made it necessary for big data applications to manage data streams and handle an impressive volume, and since these data types are complex (variety), the operation process may create different kinds of problems. Consequently, it is important to ensure the availability of big data applications. * Scalability: Scalability refers to the ability of large data applications to maintain service quality when users and data volumes increase. For a continuous stream of big data, processing systems, storage systems, etc., should be able to handle these data in a scalable manner. Moreover, the system would be very complex for big data applications. For better improvement, the system must be scalable. Paper Paper (2018) proposes a flexible data analytic framework for big data applications, and the framework can flexibly handle big data with scalability. * Reliability: Reliability refers to the ability of big data applications to apply the specified functions within the specified conditions and within the specified time. Reliability issues are usually caused by unexpected exceptions in the design and undetected code defects. For example, paper Paper (2018) uses a monitoring technique to monitor the operational status of big data applications in real time so that failures can occur in real time and developers can effectively resolve these problems. Although big data applications have many other related quality attributes, the focal ones are the five mentioned above. Therefore, these five quality attributes are critical to ensuring the quality of big data applications and are the main focus of this survey. To better perform QA of the focal quality attributes, the factors that may affect the quality attributes should be identified. We extracted the relationships existing among the big data attributes, the data quality parameters and the software quality attributes to analyze the influencing factors brought by big data attributes. We have finalized five data quality parameters, including data correctness, data completeness, data timeliness, data accuracy and data consistency. Data correctness refers to the correctness of data in transmission, processing and storage. Data completeness is a quantitative measurement that is used to evaluate how much valid analytical data is obtained compared to the planned number (Kang and others, 2018) and is usually expressed as a percentage of usable analytical data. Data timeliness refers to real-time and effective data processing (Kang and others, 2018). Data accuracy refers to the degree to which data is equivalent to the corresponding real value (Kang and others, 2018). Data consistency is useful to evaluate the consistency of given datasets from different perspectives (Kang and others, 2018). The relationships that we found on the primary studies are reported in Table 9 and discussed below. * Volume: The datasets used in industrial environments are huge, usually measured in terabytes, or even exabytes. According to (Kang and others, 2018; Kang and others, 2018), the larger the volume of the data, the greater the probability that the data will be modified, deleted and so on. In general, increased data reduces data completeness (Kang and others, 2018; Kang and others, 2018). How to deal with a massive amount of data in a very short time is a vast challenge. If these data cannot be processed in a timely manner, the value of these data will decrease and the original goal of building big data systems will be lost (Kang and others, 2018). With incorrect data, correctness and availability can not be assured. Application performance will decline as data volume grows. When the amount of data reaches a certain size, the application crashes and cannot provide mission services (Kang and others, 2018), which also affects reliability. Additionally, the volume of big data attributes will inevitably bring about the scalability issue of big data applications.