# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 6 Discussion  
### Experimental Results  
The proposed study offers several advantages over related works, including both baseline and state-of-the-art models. The results of the experiments highlight the effectiveness and robustness of the proposed model across a diverse range of datasets. By integrating product and component features with a BERT-based framework and optimizing feature selection using a top-K algorithm, the study achieves superior performance in duplicate bug report detection. In comparison to baseline models such as CNN, LSTM, CNN-LSTM, Naive Bayes, and Random Forest, the proposed model consistently outperformed these approaches across seven open-source projects, including Apache, JDT, Platform, KDE, Core, Firefox, and Thunderbird. The model achieved detection accuracies of 91.41% for Apache, 88.66% for JDT, 86.08% for Platform, 92.94% for KDE, 90.68% for Core, 88.25% for Firefox, and 91.62% for Thunderbird. These results show the model's ability to handle diverse and complex datasets while maintaining high accuracy, showcasing its robustness and scalability. Compared to state-of-the-art models, such as those leveraging dual-channel CNNs ([PERSON] et al. [14]) or clustering-based frameworks ([PERSON] et al. [23]), the proposed approach addresses several critical limitations. Specifically, while these prior methods often struggle with redundant or irrelevant features, the proposed model incorporates a top-K feature selection algorithm to systematically prioritize the most relevant linguistic features. This reduces noise, minimizes computational overhead, and enhances the overall detection performance, even in large-scale datasets. Moreover, unlike similarity-based methods (e.g., [PERSON] et al. [12] and [PERSON] et al. [26]), which primarily rely on syntactic or semantic relationships, this model uniquely integrates both lexical and semantic analyses through BERT's contextual embeddings, capturing deeper and more nuanced patterns in bug reports. Additionally, this study shows significant scalability and generalizability by validating the model across diverse open-source projects, a notable improvement over prior works like [PERSON] et al. [26], which were tested on limited repositories. The consistent performance improvements across all projects highlight the practical applicability of the model in real-world scenarios, where datasets often vary in complexity and structure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hypothesis** & \(\mathbf{p}\)**-Value** & **Result** \\ \hline H29\({}_{0}\) & (\(t\) test) 2.20 \(\times\) 10\({}^{-16}\) & H29\({}_{\text{a}}\): Accept \\ \hline H30\({}_{0}\) & (\(t\) test) 2.20 \(\times\) 10\({}^{-16}\) & H30\({}_{\text{a}}\): Accept \\ \hline H31\({}_{0}\) & (\(t\) test) 1.25 \(\times\) 10\({}^{-15}\) & H31\({}_{\text{a}}\): Accept \\ \hline H32\({}_{0}\) & (\(t\) test) 6.60 \(\times\) 10\({}^{-13}\) & H32\({}_{\text{a}}\): Accept \\ \hline H33\({}_{0}\) & (\(t\) test) 4.97 \(\times\) 10\({}^{-13}\) & H33\({}_{\text{a}}\): Accept \\ \hline H34\({}_{0}\) & (\(t\) test) 2.20 \(\times\) 10\({}^{-16}\) & H34\({}_{\text{a}}\): Accept \\ \hline H35\({}_{0}\) & (\(t\) test) 2.20 \(\times\) 10\({}^{-16}\) & H35\({}_{\text{a}}\): Accept \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The proposed model's two-step process further strengthens its advantage over other methods. By first classifying bug reports into duplicate and nonduplicate categories using product, component, and developer-related features and subsequently applying the top-K algorithm to refine feature selection, the model achieves optimal detection performance. The best results were obtained when K was set to 10, allowing the model to focus on the most significant features while discarding redundant data. This optimized feature selection process enhances the model's ability to classify bug reports with high accuracy and consistency.