## 3 Research Methodology
### Fuzzy SWARA  
Many complex problems are encountered in every area of life. Experts try to make the right decision when there are many criteria. MCDM techniques are used to select the best alternative that satisfies the objectives to the greatest extent, often meeting multiple conflicting criteria. The most crucial point in MCDM techniques is selected using weight information [1]. MCDM methods are widely used in the automotive, agriculture, energy and power, chemical, maritime, health, software, and construction sectors [71]. The SWARA method was developed to select the necessary method to resolve disputes. SWARA is an expert (decision-maker)-focused MCDM method. The main feature of the SWARA method is the ability to predict expert opinions in the process of determining the weights of the criteria [20]. The SWARA method uses fewer pairwise comparisons in prioritizing criteria and determining weights than AHP, which is frequently used among MCDM methods and gives more importance to expert opinions than other methods [72]. For this reason, confusing procedures are encountered less, and less time is spent during the application phase of the method. In addition, any expert can use their tacit knowledge, experience, and knowledge more efficiently in the SWARA method than in other MCDM methods [73]. Apart from this, it is stated that there is no need to check the consistency of the judgments since the SWARA method lists the criteria in descending order [74]. The SWARA method can be applied more simply than other MCDM methods. Although the SWARA method is subjective, it was preferred because it allows the evaluation of expert opinions. Criterion weights can be calculated by integrating the SWARA technique with fuzzy numbers to reduce the influence of subjectivity. Fuzzy set theory (FST) [75] is an approach to subjective and uncertain problems and is often applied to make decisions based on flexibility and uncertainty in data [76]. A fuzzy set is a versatile tool for both linguistic and numerical modeling. Due to the advantages of the fuzzy set, the fuzzy SWARA method was used in this study, which considers expert opinions to determine the weights of the severity subcomponents when determining the importance of errors on the project. In this study, the steps of the fuzzy SWARA method, in which fuzzy numbers are used, are explained below:  
Step 1: Criteria are listed starting from the most important. Step 2: Starting from the second criterion, the relative importance level of each criterion is determined according to Table 5. For this, criterion j is compared with the previous criterion (j \(-1\)). [PERSON] et al. [20] called this ratio \"comparative importance of the average value\" and indicated it with the symbol \(s_{j}\). Step 3: The coefficient (\(k_{j}\)) is found by Equation (1):  
\[k_{j}= \begin{cases}\overset{\sim}{1}&j=1\\ \overset{\sim}{s_{j}+1}&j>1\end{cases}  \tag{1}\]  
Step 4: The importance vector \(q_{j}\) is obtained by Equation (2):  
\[\overset{\sim}{q}_{j}= \begin{cases}\overset{\sim}{1}&j=1\\ \overset{\sim}{\bar{k}_{j-1}}&j>1\end{cases}  \tag{2}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Linguistic Terms** & **Fuzzy Number** & **Ranking** \\ \hline Very low (VL) & 0, 0, 0.25 & 5 \\ Low (L) & 0, 0.25, 0.5 & 4 \\ Medium (M) & 0.25, 0.5, 0.75 & 3 \\ High (H) & 0.5, 0.75, 1 & 2 \\ Very high (VH) & 0.75, 1, 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fuzzy member function values used. (In Equation (2), the notation \(x_{j}-1\) refers to \(q_{j}-1\)). Step 5: The weight values (\(\widetilde{w}_{j}\)) of the criteria are calculated with Equation (3):  
\[\widetilde{w}_{j}=\frac{\widetilde{q}_{j}}{\sum_{k=1}^{n}\widetilde{q}_{k}} \tag{3}\]  
where \(\widetilde{w}_{j}\), shows the importance of criterion j in a fuzzy expression. During the calculations, the expressions will be displayed as \(\text{A}_{1}=(\text{I}_{1},\text{m}_{1},\text{u}_{1})\), with triangular fuzzy numbers being \(\text{I}_{1}\leq\text{m}_{1}\leq\text{u}_{1}\).