# Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases
## 7 Performance Comparison
The \(\hat{A}_{12}\) measure indicated that the MSGA performed better than the GA and the HGA because all values are more than 0.5. For example, 80% of the time, the programs schedule.c and schedule2.c took less time than the GA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Program Name** & **Algorithm** & **No. of Test Cases** & \(\hat{A}_{12}\) (**MSGA:Other**) & **Confidence Interval** & **Coverage (\%)** \\ \hline print\_tokens.c & MSGA & 292.50 & - & (77.33, 313.67) & 93.44 \\  & GA & 405 & 0.72 & (37.37, 436.8) & 91.93 \\  & HGA & 345.50 & 0.61 & (320.71, 370.29) & 91.93 \\ print\_tokens2.c & MSGA & 373.50 & - & (357.39, 389.61) & 98.00 \\  & GA & 445 & 0.67 & (417.84, 472.16) & 96.00 \\  & HGA & 425.50 & 0.63 & (401.72, 449.28) & 97.50 \\ replace.c & MSGA & 1710.50 & - & (1351.46, 2063.54) & 92.21 \\  & GA & 1899.50 & 0.52 & (1505.39, 2293.61) & 91.39 \\  & HGA & 1894 & 0.52 & (1492.33, 2295.67) & 91.39 \\ schedule.c & MSGA & 50 & - & (50, 50) & 96.86 \\  & GA & 50 & 0.5 & (50, 50) & 96.86 \\  & HGA & 50 & 0.5 & (50, 50) & 97.12 \\ Schedule2.c & MSGA & 50 & - & (50, 50) & 97.12 \\  & GA & 50 & 0.5 & (50, 50) & 97.12 \\  & HGA & 50 & 0.5 & (50, 50) & 97.12 \\ tcas.c & MSGA & 174 & - & (164.39, 183.61) & 96.92 \\  & GA & 335 & 0.90 & (307.13, 362.87) & 90.77 \\  & HGA & 218.50 & 0.73 & (206.15, 230.85) & 96.92 \\ tot\_info.c & MSGA & 327 & - & (297.92, 356.08) & 92.25 \\  & GA & 1220 & 0.87 & (1013.18, 1426.82) & 89.15 \\  & HGA & 327.50 & 0.52 & (303.22, 351.78) & 89.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Time performance of the MSGA, GA, and HGA for the seven programs in the software under test (SUT). Note: Bold values are the best values of each program. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Program Name**} & \multicolumn{3}{c}{**Average (s)**} & \multicolumn{3}{c}{**Standard Deviation**} & \multicolumn{3}{c}{\(\hat{A}_{12}\) (**MSGA: Other**)} \\ \cline{2-9}  & **MSGA** & **GA** & **HGA** & **MSGA** & **GA** & **HGA** & **GA** & **HGA** \\ \hline print\_tokens.c & **98.21** & 112.23 & 126.95 & 18.33 & 25.23 & 29.68 & 0.66 & 0.80 \\ print\_tokens2.c & **155.15** & 173.02 & 163.37 & 23.74 & 35.06 & 29.53 & 0.66 & 0.58 \\ replace.c & **240.33** & 257.99 & 243.04 & 213.58 & 221.82 & 209.18 & 0.54 & 0.52 \\ schedule.c & **25.40** & 25.80 & 25.55 & 1.38 & 1.46 & 0.81 & 0.80 & 0.79 \\ schedule2.c & **25.00** & 25.24 & 25.17 & 0.94 & 0.34 & 0.61 & 0.80 & 0.74 \\ tcas.c & **30.95** & 35.50 & 31.42 & 3.24 & 5.47 & 2.86 & 0.84 & 0.60 \\ tot\_info.c & **151.51** & 484.64 & 165.62 & 42.59 & 324.15 & 40.48 & 0.94 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results for generating test cases using MSGA, GA, and hybrid GA (HGA). As shown in Tables 10 and 11, the performance comparison indicates that the MSGA has higher performance than other algorithms when assigning the same parameters. The improvement of the selection operator affects in generating test cases in the aspect of the time performance and coverage. MSGA can produce test case generation quicker and obtain a higher percentage of coverage. Except for the results, schedule.c and schedule2.c are quite similar because both programs are very small-scale programs.