# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Result
#### 5.5.2 Comparison Results  
In this study, the performance of the proposed model was compared with baseline models, including CNN, LSTM, CNN-LSTM, Naive Bayes, and Random Forest. The results, illustrated in Figure 8, show that the proposed model consistently outperformed the baseline models in detecting duplicate bug reports. In Figure 8, the x-axis represents duplicate detection accuracy, while the y-axis lists the baseline algorithms. The proposed model achieved the best performance across all projects, with accuracy rates of 91.41% for Apache, 84.15% for JDT, 82.94% for Platform, 90.49% for KDE, 86.21% for Core, 88.25% for Firefox, and 91.62% for Thunderbird. These improvements have meaningful implications in practical scenarios. For example, in the Apache project, achieving an accuracy of 91.41% could lead to a substantial reduction in the time and effort required for developers to manually identify duplicate bug reports. This, in turn, could save hundreds of work hours annually and ensure that developers focus on resolving high-priority issues. Similarly, in the KDE project, where an accuracy of 90.49% was achieved, the proposed model could streamline the handling of over 360,000 bug reports by accurately identifying duplicates, thereby improving resource allocation and reducing redundancy in bug resolution. Beyond accuracy, the practicality of adopting a model in real-world scenarios also depends on its training and inference times. The proposed model showed competitive training times, averaging 2.4 h per project. This duration is comparable to CNN-LSTM models and significantly shorter than traditional ensemble methods, such as Random Forest, which often require extensive preprocessing and feature engineering. Inference times were equally impressive, with the proposed model taking an average of 120 ms per bug report, making it suitable for real-time duplicate detection. In contrast, baseline methods like Naive Bayes and Random Forest often exceed 500 ms due to their reliance on large feature sets. By combining superior accuracy with efficient computational performance, the proposed model provides a robust solution for detecting duplicate bug reports in diverse software projects. This combination of strengths makes the model not only accurate but also practical for deployment in real-world software development workflows. Further comparisons were conducted using the evaluation metrics of precision, recall, and F1-measure, as illustrated in Figures 9-15. For the Apache project, the proposed model achieved 90.96% precision, 90.36% recall, and an F1-measure of 89.87%. For JDT, these values were 86.88% for precision, 84.79% for recall, and 84.00% for F1-measure. Similarly, for Platform, the model showed a precision of 85.17%, a recall of 85.09%, and an F1-measure of 89.87%, which is 86.88% for F1-measure. Figure 8: Comparison of our model and baselines. of 84.31%. In the KDE project, the model achieved 90.64% precision, 90.29% recall, and an F1-measure of 90.45%. For Core, the values were 86.23%, 86.10%, and 85.96% for precision, recall, and F1-measure, respectively. For Firefox, the model recorded a precision of 88.59%, a recall of 88.22%, and an F1-measure of 87.63%. Finally, for Thunderbird, the results were 91.40% precision, 91.77% recall, and an F1-measure of 90.98%. Across all projects, the proposed model consistently outperformed the baseline models, showcasing its superior performance in detecting duplicate bug reports. Figure 11: Comparison of baseline performance with the proposed model for Platform. Figure 9: Comparison of baseline performance with the proposed model for Apache. Figure 14: Comparison of baseline performance with the proposed model for Firefox. Figure 12: Comparison of baseline performance with the proposed model for KDE. Figure 13: Comparison of baseline performance with the proposed model for Core. Figure 15: Comparison of baseline performance with the proposed model for Thunderbird. These improvements have significant implications for real-world applications.