# Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review
## 5 Discussion
### Cross-Cutting Findings  
This subsection discusses some cross-cutting findings deduced from the key findings. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Techniques** & **Ref** & **Case Type** & **Domain** & **Metric** & **Experimental Results** \\ \hline Specification & [63] & Small & Traffic forecasting system & Delay time & Rigorous, easy and expressive \\ \hline \multirow{4}{*}{Architectural choice} & [74] & Small & Analytics-intensive big & Accidental & The effectiveness of MDD for accidental complexities \\ \cline{2-6}  & & & data applications & CPU, memory, network utilization & Improve the scalability \\ \cline{2-6}  & [97] & Small & Not mentioned & & \\  & & & & levels & \\ \cline{2-6}  & [65] & Small & Word Count application & Not mentioned & High degree of automation \\ \hline \multirow{4}{*}{Fault tolerance} & [55] & Real-world & Join bidirectional data streams & Time Consuming, Recover Ratio & Improve the performance of joining two-way streams \\ \cline{2-6}  & [46] & Real-world & MapReduce computing applications & & Memory footprint, Disk throughput, Network & Transparently enable fault tolerance for applications \\ \hline \multirow{4}{*}{Analysis} & [89] & Real-world & Scientific data compression and remote visualization & Latency, Throughput & Obtain two factors \\ \cline{2-6}  & [60] & Large & MapReduce application & Processing time, Job turnaround, Hard disk bytes written & Improve the performance \\ \hline Verification & [43] & Small & Cell Morphology Assay & MRs & Its effectiveness for testing ADDA \\ \hline \multirow{4}{*}{Testing} & [38] & Small & Network public opinion monitoring system & Response time & No specific instructions \\ \cline{2-6}  & [49] & Small & Image Processing & Error detection rate & Detects all the embedded mutants \\ \hline \multirow{4}{*}{Monitoring} & [94] & Real-world & Cloud monitoring system & Insert operation time & Achieves a response time of a few hundred \\ \cline{2-6}  & [54] & Real-world & Big data public opinion monitoring platform & Accuracy & Rate, Flapsed time & High accuracy and meeting the requirements of real time \\ \cline{2-6}  & [116] & Large & No specific instructions & Throughput, Retad latency, Write latency & Improve the performance by changing various tuning parameters \\ \cline{2-6}  & [39] & Small & Big data-based condition monitoring of power apparatuses & No specific instructions & Improve the accuracy of condition monitoring \\ \hline \multirow{4}{*}{Prediction} & [108] & Small & Cloud computing system & False Positive Rate, true positive rate & Achieve high true positive rate, low false positive rate for failure prediction \\ \cline{2-6}  & [115] & Small & Different applications & Prediction accuracy & New prediction system is accurate and efficient \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Experimental summary and statistics. * Relations between big data attributes and quality attributes. The collected results based on main findings show that big data attributes sometimes have contradictory impacts on quality attributes. Some big data attributes are found to improve some quality attributes and weaken others. These findings lead to a conclusion that big data attributes do not always improve all quality attributes of big data applications. To a certain extent, this conclusion matches the definition of big data attributes stated by most of the researchers involved in a study regarding the challenges and benefits between big data attributes and quality attributes in practice. * Relations among big data attributes, quality attributes and big data applications. In this study, researchers have proposed some quality attributes to effectively assess the impacts of big data attributes on applications. Therefore, we believe that it is incorrect to limit the research on the quality of big data applications to a certain big data property, obtain some negative results and then state a general conclusion that comprehensive consideration of big data attributes causes big data applications' quality to weaken.