# Systematic Literature Review on Test Case Selection and Prioritization: A Tertiary Study
## 4 Results and Analysis
### RQ3. How Have the Secondary Studies Benefitted from the Existing Tools and Tests? **RQ 3.1:**_Have statistical tests been used in the secondary studies?_  
Table 5 shows the details of the statistical tests in each study. The details include the area or purpose of the test, the reference of the studies where it was used, and the number of times it was used in the 22 surveyed papers. Figure 10 represents a bar graph revealing the scarcity in the usage of statistical tests in these secondary studies in TCS&P. Only four of the 22 surveyed studies mentioned the statistical tests used in the area. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Test Name** & **Area** & **References** & **Count** \\ \hline \(1\) & Bonferroni Test & Multiple Comparison for analyzing data & [1; 63; 64; 65; 4] & 5 \\ \hline \(2\) & Chi-Square & Extract the features of a test case & [66] & 1 \\ \hline \(3\) & Vargha and Delaney Test & Performance Analysis of the algorithm and Effect Size Measurement & [67; 68; 69; 70; 71; 72; 73; 74] & 8 \\ \hline \(4\) & Mann–Whitney & Pairwise comparison for Performance Analysis of the algorithm & [67; 68; 69; 70; 75; 76] & 6 \\ \hline \(5\) & Spearman’s Correlation & Performance Analysis of the algorithm & [77; 78; 79] & 3 \\ \hline \(6\) & [PERSON]’s test & Performance Analysis of the algorithm & [72] & 1 \\ \hline \(7\) & Wilcoxon t-test & Performance Analysis of the algorithm & [72] & 1 \\ \hline \(8\) & Two-Way permutation & Evaluation of execution time of algorithms & [80] & 1 \\ \hline \(9\) & Kruskal Wallis &
\begin{tabular}{c} To Study the statistical difference of running time of search \\ algorithms and Multiple Comparison for the Performance \\ Analysis of the algorithm \\ \end{tabular} & [76; 77] & 2 \\ \hline \(10\) & Anova & Multiple Comparison for the Performance Analysis of the algorithm & [81; 82; 83; 84; 85; 86; 87] & 7 \\ \hline \(11\) & LSD & Performance Analysis of the algorithm & [81] & 1 \\ \hline \(12\) & [PERSON]’s Test & Multiple Comparison for the Performance Analysis of the algorithm & [76] & 1 \\ \hline \(13\) & Scott–Knott Analysis & Multiple Comparison for the Performance Analysis of the algorithm & [74] & 1 \\ \hline \(14\) & [PERSON]’s Delta & Effect Size Measurement & [74] & 1 \\ \hline \(15\) & [PERSON]’s HSD & Multiple Comparison for the Performance Analysis of the algorithm & [83; 87] & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical tests used in secondary studies summary. Figure 10: The number of statistical tests used in various surveys. Figure 11 shows a doughnut graph to provide better insight into the application of statistical tests. The performance analysis of the techniques proposed by the researchers used most of the tests (approximately 74%). The graph depicted in Figure 12 clearly shows that ANOVA, Mann-Whitney, Vargha and Delaney, and Bonferroni tests were the most used in TCS&P. Table 4 provides a quick reference to the studies where the usage of these tests was recorded. Unfortunately, this analysis indicates a major gap in the usage of statistical tests in regression TCS&P. This provides an opportunity for researchers who could make future use of statistical tests for qualitative assessment, validation, and comparison of the techniques used in TCS&P. _**RQ 3.2:** Which tools have been used by the secondary studies? Table 6 summarizes the information about the open-source behavior, available download links, and the referring studies of the mentioned research tools.