# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Result
#### 5.5.2 Comparison Results
For example, the high precision and recall rates achieved in the Apache project indicate that the model can effectively minimize both false positives and false negatives in duplicate detection. This could translate into fewer redundant bug reports being mistakenly flagged or overlooked, saving hundreds of hours of manual effort annually for project managers. Similarly, in the KDE project, where the F1-measure reached 90.45%, the model's high accuracy in identifying duplicate reports ensures a more streamlined workflow for managing large repositories of bug reports, potentially reducing the time-to-resolution for critical issues. The practical applicability of the proposed model also depends on its computational efficiency, including training and inference times. Training the model required an average of 2.4 h per project, which is comparable to CNN-LSTM models but significantly faster than ensemble methods like Random Forest, which often require extensive preprocessing. Inference times were also optimized, averaging 120 ms per bug report. This makes the model suitable for real-time duplicate detection in large-scale continuous integration and deployment pipelines. In contrast, traditional methods like Naive Bayes and Random Forest often exceed 500 ms per report due to their reliance on a larger feature space, making them less efficient in real-time scenarios. By achieving a balance between accuracy, precision, recall, and computational efficiency, the proposed model provides a practical and scalable solution for managing duplicate bug reports across diverse software projects. These results show that the model is well-suited for deployment in real-world software development environments, where both performance and efficiency are critical considerations. To statistically verify the significance of the observed differences between the proposed model and the baseline models, a series of hypothesis tests were conducted. The null hypothesis (H\({}_{0}\)) posited that there was no significant difference in duplicate detection performance between the proposed model and each baseline, while the alternative hypothesis (H\({}_{a}\)) suggested the presence of a significant difference. The normality of the data was first tested. If the \(p\)-value exceeded 0.05, a \(t\)-test was performed; otherwise, the Wilcoxon test was used. For example, for the Apache project compared to the CNN model, the \(p\)-value was 8.77 \(\times\) 10\({}^{-10}\), which is significantly lower than 0.05, leading to the rejection of the null hypothesis and acceptance of the alternative hypothesis. This result indicated that the proposed model performed significantly better than CNN in duplicate detection for Apache. Similar results were observed across all projects and baseline models, with \(p\)-values consistently below 0.05. For statistical verification, the following null and alternative hypotheses were established to evaluate whether the proposed model exhibited a significant difference in duplicate detection performance compared to the baseline models (CNN, LSTM, CNN-LSTM, Naive Bayes, and Random Forest) across all datasets. The null hypotheses (H\({}_{0}\)) are as follows:  
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2. Apache Dataset: * H1\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN. * H2\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and LSTM. * H3\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN-LSTM. * H4\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Naive Bayes. H5\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Random Forest. 2. JDT Dataset: * H6\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN.