# Systematic Literature Review on Test Case Selection and Prioritization: A Tertiary Study
## 4 Results and Analysis
### RQ5. What Are the Probable Prospects in TCS&P?
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**S. No.**} & \multicolumn{2}{c}{**Evolutionary Technique**} & \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Reference**} \\  & & & S1(2010), S4(2012), \\  & & & S6(2012), S7(2014), \\  & & & S8(2015), S9(2016), \\ \(1\) & & & S10(2016), S11(2017), \\  & & & S12(2017), S13(2017), \\  & & & S14(2018), S15(2018), \\  & & & S17(2019), S18(2019), \\  & & & S19(2019), S20(2019) \\ \hline
2 & NSGA-II & S1(2010), S19(2019) & [71; 77; 80; 130] \\ \hline
3 & vNSGA-II & S1(2010) & [130] \\ \hline
4 & PSO & S7(2014), S11(2017), \\  & & S15(2018), S20(2019) & [77; 131; 132; 133; 134; 135; 136; 137] \\ \hline
5 & AVM(1+1) & S9(2016) & [78] \\ \hline
6 & Evolutionary & S9(2016) & [78] \\  & Algorithm & & \\ \hline
7 & Hill Climbing & S10(2016), S14(2018) & [81] \\ \hline
8 & ACO & S12(2017), S14(2018), \\  & & S15(2018), S17(2019), \\  & & S20(2019) & [138; 139; 140; 141; 142; 143; 144] \\ \hline
9 & String Distance & S12(2017) & [145] \\ \hline
10 &
\begin{tabular}{c} Bacteriological \\ Algorithm \\ \end{tabular} & S15(2018) & [146] \\ \hline
11 &
\begin{tabular}{c} Bee Colony \\ Optimization \\ \end{tabular} & S15(2018) & [141] \\ \hline
12 & ABC & S19(2019) & [147] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Details of evolutionary techniques surveyed in the 22 secondary studies. (GA—genetic algorithms, PSO—particle swarm optimization, AVM—alternating variable method, EA—evolutionary algorithms, HC—hill climbing, ACO—ant colony optimization, SD—string distance, BA—bacteriological algorithm, BCO/ABC—bee colony optimization/artificial bee colony). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**S. No.**} & \multicolumn{1}{c}{**Category**} & \multicolumn{1}{c}{**Studies in Which Mentioned**} \\ \hline
26 & Test input-based & S3, S16 \\ \hline
27 & Change/modification-based & S6, S13, S20 \\ \hline
28 & Bayesian network-based & S17 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._The initial secondary text in TCS&P with an evolutionary technique (GA) was S1. Subsequently, many more techniques have been reviewed. Figure 19 depicts the number of evolutionary techniques surveyed per year from 2010 until 2020. After 2015, the number of surveyed evolutionary techniques experienced rapid growth. This implies an even more rapid increase in the number of available primary texts using evolutionary techniques for TCS&P. This will provide support to researchers who use evolutionary techniques in TCS&P in future works. Notably, the number of secondary studies mentioning evolutionary techniques also rose after 2015 (see Figure 20). This is evident from the fact that evolutionary techniques increased in number for TCS&P after 2015 (Figure 20) and that the number of secondary studies conducted in TCS&P also rose post-2015 (Figure 3). It can also be easily inferred that the extensive use of evolutionary techniques in TCS&P is increasing. Another relevant finding is that GA is the most commonly applied evolutionary technique in TCS&P (see Figure 21), and has been used in 16 secondary studies. GA and its variants are popular in obtaining solutions to many other real-world problems [148; 149]. The two next highest ranking are ACO and PSO. Additional new techniques are being  
Figure 19: The number of evolutionary techniques surveyed per year from 2010 to 2019. Figure 20: Number of secondary studies mentioning evolutionary techniques by year. developed while earlier approaches continue to be used. All these findings provide incentive for researchers to work on evolutionary techniques in TCS&P.