## 4 Proposed Methodology
### Textual Data Extraction from Pull Requests
The following formula is used to calculate how well each population member did in presenting the candidate solution:  
\[p_{j}=\frac{f_{j}-f_{worst}}{\sum_{i=1}^{n}{(f_{j}-f_{worst})}},\ \ j\ \in\ \{1,\,2,\,\ldots n\}. \tag{4}\]  
The below equation determines each member's participation coefficients:  
\[QC_{j}=\frac{p_{j}}{p_{j}+p_{best}+p_{K}} \tag{5}\]  
\[QC_{best}=\frac{p_{best}}{p_{j}+p_{best}+p_{K}} \tag{6}\]  
\[QC_{K}=\frac{p_{K}}{p_{j}+p_{best}+p_{K}} \tag{7}\]  
where \(QC_{K}\), \(QC_{best}\), \(QC_{j}\) are the participation coefficients of the \(K\)th member (\(K\) is an integer determined randomly from the set ({[1, 2, \ldots, N]})\) and the best member-th member, respectively, during hybrid layer formation. In addition, i, k \(\in\) {1, 2, \(\ldots\), N}, k = i, qi is the quality of the \(j\)th candidate solution, and \(f_{worst}\) is the value of the objective function of the worst candidate solution. For each person in the population, the hybrid leader is constructed after the participation coefficients have been determined. We have  
\[Gl_{j}=QC_{j}\cdot Y_{j}+QC_{best}\cdot Y_{best}+QC_{K}\cdot Y_{K}, \tag{8}\]  
where, \(Y_{K}\) is a randomly chosen population member and \(Gl_{j}\) is the \(j\)th member's hybrid leader. This member's row number in the population matrix is represented by the index \(K\). The HLBO algorithm guides the calculation of each population member's new position within the search space. If the goal function value is higher than it was in the prior position, the corresponding member will accept the new location; otherwise, it will remain in the previous place. We have  
\[y_{j,i}^{new,Q1}=\left\{ \begin{array}{ll}y_{j,i}+R\cdot(Gl_{j,i}+I\cdot y_{ j,i}),&f_{Gl_{j}}<f_{j};\\ y_{j,i}+R\cdot(y_{j,i}-Gl_{j,i}),&else,\end{array} \right. \tag{9}\]  
\[Y_{j}=\left\{ \begin{array}{ll}Y_{j}^{new,Q1},&f_{j}^{new,Q1}<f_{j};\\ Y_{j},&else,\end{array} \right., \tag{10}\]  
where, the first phase of HLBO gives the objective function \(f_{j}^{new,Q1}\). The parameter \(Y_{j}^{new,Q1}\) denotes the \(j\)th member's new position. \(Y_{j,i}^{new,Q1}\) is its \(i\)-th dimension. In addition, real random number 'r' is chosen at random from the range [0, 1], which represents the objective function value produced from the \(j\)th member's hybrid leader. Furthermore, \(I\) represents the randomly selected integer over the interval [1, 2], and \(j\). Each member of the population has a neighborhood that is taken into account by the HLBO algorithm. Each member can switch positions in this neighborhood by doing local searches to find a place with a higher value for the objective function. This local search is meant to improve and boost the HLBO algorithm's ability to be used. We have  
\[Y_{j,i}^{new,Q2}=y_{j,i}+(1-2r)\cdot r\cdot\left(1-\frac{s}{S}\right)\cdot y_{ j,i} \tag{11}\]  
\[Y_{j}=\left\{ \begin{array}{ll}Y_{j}^{new,Q2},&f_{j}^{new,Q2}<f_{j};\\ Y_{j},&else,\end{array} \right. \tag{12}\]  
Based on the second phase of HLBO, \(R\) is a constant equal to 0.2, \(S\) is the maximum number of iterations, \(s\) is the iteration counter, \(Y_{j,i}^{new,Q2}\) is the new position of the \(j\)th member. \(f_{j}^{new,Q2}\) is the objective function value, and \(Y_{j,i}^{new,Q2}\) is its \(i\)th dimension. Algorithm 1 depicts the working process of textual data extraction from pull requests using HLBO. ```
Input : \(la_{i}\) and \(ua_{j}\) Output : \(Y_{j,i}^{new,Q2}\)
1. Initialize the random population
2. Adjust n and s.
3. Set constraints of the problem variables \(y_{j,i}=la_{j}+R\cdot(ua_{j}-la_{i})\),
4. If \(j=0\) and \(i=1\)
5. Compute candidate solution \(p_{j}=\frac{f_{j}-f_{worst}}{\sum_{i=1}^{n}\left(f_{j}-f_{worst}\right)}\),
6. Define the population \(Gl_{j}=QC_{j}\cdot Y_{j}+QC_{best}\cdot Y_{best}+QC_{K}\cdot Y_{K}\)
7. Update the condition using \(y_{j,i}^{new,Q1}=\left\{ \begin{array}{ll}y_{j,i}+R\cdot(Gl_{j,i}+I\cdot y_{ j,i}),&f_{Gl_{j}}<f_{j};\\ y_{j,i}+R\cdot(y_{j,i}-Gl_{j,i}),&else,\end{array} \right.\)
8. Update the final values
9. End else
10.