# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Result  
#### 5.5.1 Result of Our Approach  
The performance of the proposed model in detecting duplicate bug reports is depicted in Figure 6.  
The x-axis in the presented figures corresponds to the number of top-K features selected by the feature selection algorithm, varying from 1 to 10, while the y-axis reflects the accuracy achieved by the model. Notably, the model showed its best performance when the top-K value was set to 10. Specifically, the Apache project achieved an accuracy of 91.41%, with the JDT and Platform projects recording 88.66% and 86.08%, respectively. The KDE project attained the highest accuracy at 92.94%, while the Core, Firefox, and Thunderbird projects reached 90.68%, 88.25%, and 91.62%, respectively. When averaged across all K values, the accuracy for the Apache project was 84.86%. Similarly, the JDT and Platform projects achieved average accuracies of 84.15% and 86.21%, respectively. The KDE project achieved an average accuracy of 86.39%, while the Core, Firefox, and Thunderbird projects  
Figure 6: Performance results of the proposed model. attained averages of 89.04%, 88.15%, and 89.73%, respectively. Notably, with the top-K value set to 10, the performance improved by 6.55%, 4.51%, 3.14%, 2.45%, 4.47%, 1.86%, and 2.58% for Apache, JDT, Platform, KDE, Core, Firefox, and Thunderbird, respectively. The observed performance improvements hold significant implications in real-world applications. For instance, in large-scale software projects, a 6.55% accuracy improvement for the Apache project could save hundreds of developer hours annually by efficiently identifying duplicate bug reports. Similarly, the KDE project's accuracy gains of 2.45% enhance the precision in identifying redundancies across more than 360,000 bug reports, thereby streamlining the issue-tracking workflow and improving the allocation of development resources. Such improvements reduce the time and effort required to triage bug reports, allowing developers to focus on resolving high-priority issues more effectively. Additionally, training and inference times are critical factors for practical adoption. The proposed model showed competitive training efficiency compared to traditional models like CNNs and LSTMs. The optimized feature selection process significantly reduced computational overhead, enabling faster inference times during duplicate detection. For example, in the KDE project, feature selection reduced the input space, allowing the model to perform inference in milliseconds, ensuring real-time applicability in continuous integration pipelines. On average, the model required 2.4 h of training time per project using standard resources. This is comparable to CNN-LSTM hybrids but significantly shorter than baseline ensemble models like Random Forests, which involve extensive feature engineering and computational costs. The inference time for the proposed model averaged 120 ms per bug report, outperforming traditional algorithms such as Naive Bayes and Random Forest, which often exceed 500 ms due to their reliance on large feature sets. These findings highlight that the proposed model not only achieves superior accuracy but also maintains computational efficiency, making it a practical solution for duplicate detection in diverse software projects. The combination of enhanced accuracy and reduced resource requirements positions the model as a reliable and scalable tool for real-world deployment. Figure 7 illustrates the impact of the feature selection algorithm on the detection of duplicate and nonduplicate bug reports. The x-axis represents whether the feature selection algorithm was applied, while the y-axis indicates the performance of the model. The results clearly show that the feature selection algorithm significantly improved the model's duplicate detection performance. Specifically, the accuracy increased by 41.72% for the Apache project, 37.94% for the JDT  
Figure 7: Comparison of Our Model and Non-Algorithm.