# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 7 Conclusions  
The ability to determine whether a newly submitted bug report represents a new issue or a duplicate of an existing one is a critical aspect of software maintenance. Automated bug detection algorithms reduce redundant efforts and minimize the likelihood of human errorsby project managers and developers. This study addresses this challenge by integrating product and component features into a BERT-based framework, coupled with a top-K feature selection algorithm, to classify duplicate and nonduplicate bug reports effectively. The top-K algorithm extracts the most relevant feature words from bug report data and optimizes the selection process, enabling the BERT model to focus on key linguistic patterns while leveraging contextual relationships derived from product and component information for accurate duplicate detection. The proposed model's performance was rigorously evaluated across seven open-source projects, including Apache, JDT, Platform, KDE, Core, Firefox, and Thunderbird. The model achieved detection accuracies of 91.41%, 88.66%, 86.08%, 92.94%, 90.68%, 88.25%, and 91.62%, respectively, consistently outperforming baseline models such as CNN, LSTM, CNN-LSTM, Naive Bayes, and Random Forest. Compared to these baseline models, the proposed approach showed substantial accuracy improvements, with gains of 40.67%, 33.82%, 32.64%, 39.31%, 36.83%, 38.24%, and 41.17% for Apache, JDT, Platform, KDE, Core, Firefox, and Thunderbird, respectively. These results show the model's robustness and scalability in handling diverse and complex datasets. One of the key differentiators of this study is the utilization of product and component features, which sets it apart from prior works. While approaches such as [PERSON] et al. [14]'s dual-channel CNN and [PERSON] et al. [23; 40]'s clustering-based frameworks primarily rely on textual patterns, the inclusion of product and component information enhances the model's ability to capture contextual relationships within bug reports more effectively. Additionally, the proposed model addresses the limitations of similarity-based methods like [PERSON] et al. [12; 16; 19] and [PERSON] et al. [26], which focus on either syntactic or semantic relationships. By leveraging BERT's contextual embeddings, this model integrates both lexical and semantic analyses, capturing deeper and more nuanced patterns in bug reports. The integration of the top-K feature selection algorithm is particularly impactful, allowing the model to systematically prioritize the most informative features, including product and component details, while reducing noise and improving computational efficiency. This optimization enables the model to deliver high performance even on large-scale datasets. By eliminating irrelevant data and focusing on key features, the proposed approach ensures consistent and accurate classification results. Statistical analyses further validated the model's superior performance, showing significant improvements over baseline and state-of-the-art models across all datasets. The results highlight the generalizability and robustness of the proposed model in addressing the challenges of duplicate bug report detection in diverse software projects. By leveraging product and component information in combination with advanced feature selection, the model provides a scalable and efficient solution for real-world software maintenance tasks. Future work will focus on extending the scope of this study by incorporating datasets with varying structures and reporting conventions, as well as exploring additional features to further enhance the model's applicability. Advanced hyperparameter tuning and the exploration of new methodologies will also be prioritized to improve scalability and adaptability across broader contexts. These efforts aim to refine the model's ability to handle more complex scenarios and provide greater utility in automated bug detection.