## 4 Application
Since the project team generally prioritizes and ranks errors identified in software project tests, MCDM methods used in problems with multiple decision-makers (experts) were preferred in this study. Figure 1 shows the flow chart of the study. Errors in the software tests used in the application were obtained from the test records using the convenience sampling method, taking into account the research objectives. The classical FMEA method was first used to prioritize the errors in the test records. In Appendix A, the probability (P), detectability (D), and severity (S) values of the errors are determined, and the RPN value was calculated according to these values. It is obvious that evaluations made by a single person will be insufficient due to personal biases and lack of knowledge. In addition, there is no definitive formula for determining the appropriate sample size for decision-makers [80]. The fuzzy SWARA method is largely based on decision-makers' opinions, which reveals subjectivity. In order to reduce this subjectivity, it is essential to have more than one decision-maker and to have both practical and theoretical expertise in software testing. Therefore, in this study, decision-makers are not necessarily the same as the ones used in the analysis. Figure 1: Flow chart of the study. makers who meet these criteria were selected using a purposive sample enriched with snowball recruitment. In order to determine the weights of the impact subcomponents used in the proposed model, decision-makers consisting of a technical manager experienced in software projects, a software testing engineer, and an academician who consults on software projects were determined. The order of importance determined by decision-makers for the sub-component is shown in Table 6.  
Table 7 shows the fuzzy triangular number matrix and \(p_{j}\) (arithmetic average of l, m, u values determined by decision-makers for each component) values of the subcomponents according to the order determined by the decision-makers. Table 8 shows the comparative importance (\(s_{j}\)), coefficient value (\(k_{j}\)), and importance vector (\(q_{j}\)) of the average value calculated according to Equations (1) and (2). Table 9 shows the weight values of the severity subcomponents calculated according to Equation (3) and the clarified weights calculated according to Equation (4). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subcomponent** & **DM1** & **DM2** & **DM3** \\ \hline Time (S1) & 1 & 1 & 1 \\ Quality (S2) & 3 & 4 & 2 \\ Cost (S3) (person-month) & 2 & 5 & 4 \\ Solvability (S4) & 5 & 2 & 5 \\ Usability (S5) & 4 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Order of importance determined by decision-makers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sub-Component**} \\ \hline  & \(\mathbf{S_{1}}\) & \(\mathbf{S_{2}}\) & \(\mathbf{S_{3}}\) & \(\mathbf{S_{4}}\) & \(\mathbf{S_{5}}\) \\ \hline DM1l & 0.75 & 0.25 & 0.5 & 0 & 0 \\ DM1m & 1 & 0.5 & 0.75 & 0 & 0.25 \\ DM1u & 1 & 0.75 & 1 & 0.25 & 0.5 \\ DM2l & 0.75 & 0 & 0 & 0.5 & 0.25 \\ DM2m & 1 & 0.25 & 0 & 0.75 & 0.5 \\ DM2u & 1 & 0.5 & 0.25 & 1 & 0.75 \\ DM3l & 0.75 & 0.5 & 0 & 0 & 0.25 \\ DM3m & 1 & 0.75 & 0.25 & 0 & 0.5 \\ DM3u & 1 & 1 & 0.5 & 0.25 & 0.75 \\ \(p_{j}l\) & 0.75 & 0.25 & 0.1667 & 0.1667 & 0.1667 \\ \(p_{j}m\) & 1 & 0.5 & 0.3333 & 0.25 & 0.4167 \\ \(p_{j}u\) & 1 & 0.75 & 0.5833 & 0.5 & 0.6667 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fuzzy triangular number matrix.