# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 2 Related Work
This study introduces a novel approach that combines a top-K feature selection algorithm with a BERT-based framework to significantly advance the field of duplicate bug report detection. Specifically, the proposed approach integrates product and component features into the BERT model and optimizes feature selection, resulting in superior accuracy in duplicate detection across multiple datasets. By effectively capturing the nuanced contextual relationships within bug reports, this method achieves improved classification performance compared to prior approaches. Unlike studies such as [PERSON] et al. (2018) and [PERSON] et al. (2016), which focused on fine-tuning BERT for general NLP tasks, this study tailors BERT specifically for the domain of duplicate bug report detection. By leveraging product and component information along with the top-K feature selection method, the proposed approach not only enhances the ability to identify duplicate reports but also reduces computational noise and improves efficiency. This optimization process ensures that the model prioritizes the most relevant linguistic features, thereby addressing the challenges posed by redundant or irrelevant features. In addition, the study tackles limitations observed in prior works, such as those by [PERSON] et al. (2018) and [PERSON] et al. (2018), which either overlooked redundant features or relied on less efficient feature extraction techniques. By systematically optimizing feature selection, the proposed model eliminates noise and enhances computational efficiency while maintaining high accuracy. Furthermore, unlike approaches such as [PERSON] et al. (2019), which were validated on limited datasets, the proposed method is evaluated on diverse open-source projects, including Apache, JDT, and KDE, showing its robustness and scalability in real-world scenarios. The proposed approach also advances the state of the art by combining lexical and semantic analysis for precise and consistent detection of duplicate bug reports. While [PERSON] et al. (2018)'s dual-channel CNN approach and [PERSON] et al. (2019)'s similarity-based metrics focus on either syntactic or semantic relationships, this study leverages BERT's contextual embeddings to integrate both aspects. This dual-layered analysis captures deeper relationships between bug reports, ensuring more accurate and consistent results. Moreover, comprehensive benchmarking against established baselines and state-of-the-art models highlights consistent improvements in precision, recall, and F1 scores, underscoring the model's effectiveness in performance and efficiency. Through these advancements, the proposed study addresses critical limitations in existing methods and establishes a scalable, efficient, and accurate solution for detecting duplicate bug reports. By integrating product and component features with advanced feature selection and leveraging BERT's contextual capabilities, this study provides a significant contribution to the field, enabling better bug tracking and software maintenance practices. We provide a comparative analysis of duplicate bug report detection approaches, as shown in Table 1.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
The table presents a comparative analysis of various duplicate bug report detection approaches. This comparison highlights the methodologies, datasets, performance metrics, and key contributions or limitations of each study, including the proposed method.