# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Evaluation Metrics  
To evaluate the performance of the proposed model for duplicate bug report detection, the following evaluation metrics were employed [38; 39]:  
\[\text{Acc\_Dup}=\frac{\text{True Positive}+\text{True Negative}}{\text{Total of Bug Reports}} \tag{1}\]  
This metric calculates the proportion of correctly classified bug reports (both duplicate and nonduplicate) out of the total number of bug reports. It provides an overall measure of the model's prediction accuracy.  
\[\text{Rec}_{\text{Dup}}=\frac{\text{True Positive}}{\text{TPTrue Positive}+\text{False Negative}} \tag{2}\]  
Recall measures the model's ability to correctly identify duplicate bug reports among all actual duplicate reports. A higher recall indicates fewer duplicate bug reports being misclassified as nonduplicates.  
\[\text{Pre\_Dup}=\frac{\text{True Positive}}{\text{True Positive}+\text{False Positive}} \tag{3}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **APACHE** & **JDT/PLATFORM** \\ \hline \multirow{4}{*}{Status} & FIXED/NOT A BUG/INTENTIONAL/LATER/ & FIXED/INVALID/WONTFIX/ \\  & REMIND/DUPLICATE/WORKSFORME/ & \multirow{2}{*}{INACTIVE/DUPLICATE/WORKSFORME/} \\  & MOVED/UPSTREAM/DOWNSTREAM/ & \\  & WAITINGFORINFO/BACKTRACE/ & \\  & UNMAINTAINED & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.  
Precision evaluates the proportion of correctly identified duplicate bug reports out of all bug reports predicted as duplicates. A higher precision indicates fewer nonduplicate bug reports being incorrectly classified as duplicates.  
\[\text{F1\_Dup}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
The F1 score is the harmonic mean of precision and recall. It balances the trade-off between these two metrics, especially when the dataset has an imbalance between duplicate and nonduplicate bug reports. A high F1 score indicates that both precision and recall are performing well without favoring one over the other.  
Definitions of Key Terms:  
* True Positive (TP): Cases where the model correctly identifies a duplicate bug report as a duplicate.
* True Negative (TN): Cases where the model correctly identifies a nonduplicate bug report as nonduplicate.
* False Positive (FP): Cases where the model incorrectly classifies a nonduplicate bug report as a duplicate.
* False Negative (FN): Cases where the model incorrectly classifies a duplicate bug report as nonduplicate.  
Significance of the Metrics:  
These metrics collectively offer a comprehensive evaluation of the model's predictive performance:  
* Accuracy (Acc_Dup) provides an overall snapshot of the model's correctness.
* Recall (Rec_Dup) emphasizes the model's ability to minimize missed duplicate detections.
* Precision (Pre_Dup) focuses on the reliability of duplicate predictions.
* F1 Score (F1_Dup) ensures a balanced evaluation, which is particularly useful in datasets with an unequal distribution of duplicate and nonduplicate reports.  
By employing these metrics, the study provides a rigorous assessment of the model's effectiveness in identifying duplicate bug reports, ensuring both its reliability and practical applicability.