## 4 Results
Regarding the data presented in Figure 11, it is worth mentioning that the M1 group has a total of 19 studies that used single metrics, comprising a total of two unique metrics (HV and IGD) with a frequency of 19; however, 18 of them were HV, while the remaining one was IGD. In the same figure, the rest of the groups maintain a good diversity of metrics. This indicates that few studies utilized multiple metrics in their research, but the total number of unique metrics was high. For example, studies that employed more than four metrics employed the highest number of metrics: M3 = 16, M4 = 11, M5 = 15, and M6 = 10 metrics. RQ6: What are the number of objectives used in the studies? Figure 12 shows the number of objective functions or problems formulated in the community. As above-mentioned there are MOPs with no more than three objectives (2 and 3) and MaOPs with more than three objectives. As shown in Figure 12, two and three objectives are the most re-formulated problems, while there are increasing interest in the community in formulating MaOPs compared to previous studies' review [19], although there are number of studies that formulated a different number of objectives in a single study, such as References [75; 76; 86; 91; 95; 96; 102; 108; 109; 117; 122; 123; 138; 146; 147]. Table 5 references of these objectives are stated. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study Group** & **  
RQ7: What are the applied areas in SE of the studies? To answer this question, we adopt the grouping made by the previous related studies [19; 20]. Figure 13 shows the studies' distribution of common software engineering areas. As shown in the graph, software testing is the most applied area, while the graph shows a decline for software design, requirements, management, and verifications, respectively. This also indicates that the SBSE community practically applied many applicable areas in SE fields, and we believe some are still not mature, yet they are gaining popularity in the community. On the other hand, some questions may arise regarding the popularity of software testing or design. There are many convincing facts, and some are historically related. For example, early SBSE studies were on software testing, and this might lead to a new research gap or discoveries that result in further investigation. Another fact is that software testing is a perfect fit for automation that might be applicable to SBSE, as well, although testing activities are considered the most expensive in SE in terms of time, cost, and resources. Regarding this aspect, practitioners might prefer to optimize conflicting objectives, while SBSE pioneers believe the metric richness in SE fields is a perfect fit for applying search-based methods. However, it is unknown if testing and design have more metrics compared to other fields. Table 6 lists the references for applied areas in SE. Figure 12: Studies using number of objectives. \begin{table}
\begin{tabular}{l l} \hline \hline
**Number of Objective** & **Reference** \\ \hline Two Objectives & [7,46,47,50,51,56,57,62–64,67,69,72–74,76,80,81,84,86,88–91,95,99,100,104,106,108,109,113–115,119–121,123–126,130–132,134,137–140,142,144] \\ Three Objectives & [49;53;55;66;68,70,71,75–78,82,83,85–87,91,94,95,97,102,103,105,107,109,110,116–118,122–125,127–129,136,145] \\ Four Objectives & [3;48;52;54;58;60;65;75,79;96,102,108,117,122,133,138,141,146,147] \\ Five Objectives & [6;59;61;93;98;101;109;133;135;143,146,147] \\ Six Objectives & [92] \\ Seven Objectives & [96] \\ Nine Objectives & [111;112] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of objectives used in the studies and their references list. RQ8: What are the performance metrics distribution in each SE applied areas?