# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 2 Related Survey  
Many existing research works on bug deduplication and triage primarily focus on bug reports. These methods generally require the involvement of domain experts, and automated methods have shown limited accuracy. [PERSON] et al. compared IR-based and ML-based methods for bug report deduplication [12], and the experimental results showed no significant difference in terms of accuracy or runtime efficiency. [PERSON] et al. conducted a quantitative analysis of commonly used bug classification methods, including signature-based approaches (such as functions, addresses, and linked libraries) and text-tokenized methods. The results indicated that IR methods based on TF-IDF had better triage effectiveness. [PERSON] et al. surveyed bug prioritization works based on bug reports up to 2017 [13], covering various methods, such as data mining and machine learning techniques for bug identification, clustering, classification, and rating, including supervised and unsupervised methods. The advantages and limitations of the methods were analyzed, with the authors concluding that there is still room for improvement in current approaches. However, this work is relatively early, focusing only on bug prioritization using bug reports. Our work primarily focuses on research conducted after 2015 to ensure the survey's relevance and up-to-dateness. [PERSON] et al. conducted a survey on bug classification work based on bug reports [14], summarizing various related techniques, such as text-based classification, recommendation-based approaches, and tossing-graph-based methods. [PERSON] et al. summarized commonly used features and general steps in bug report deduplication [15], highlighting the existing issues and potential research points for optimization. However, the coverage of these articles is not comprehensive, and they do not specifically focus on runtime information for classification. [PERSON] et al. surveyed classification methods based on machine learning, profiles, or metadata, comparing and discussing the pros and cons of different approaches [16]. They concluded that no single method has advantages in all dimensions and provided insights into potential research points such as the cold activation problem and load balancing. [PERSON] et al. briefly described the factors to consider in bug triage and listed methods and contributions of some bug triage-related works [17]. [PERSON] et al. provided a general description of the processes for IR-based and machine-learning-based methods [18], along with relevant works listed separately. [PERSON] et al. surveyed deduplication methods based on natural language processing (NLP) [7], information retrieval (IR), and clustering, as well as classification techniques using naive Bayes combined with machine learning. They suggested focusing on improving the efficiency of deep learning models for precise bug report classification in future research. These works primarily focus on investigating specific categories of techniques, while our work encompasses both common information retrieval (IR) and machine learning (ML)-based techniques, providing a comprehensive investigation into both categories of approaches. [PERSON] et al. quantitatively analyzed bug triage using common machine learning methods and tested six approaches [19], including SVM, naive Bayes, and random forest. The experimental results showed that SVM performed better in terms of F-measure, average accuracy, and weighted average F-measure metrics. However, it is worth noting that this article focused on a binary classification problem, which differs from bug triage. [PERSON] et al. mainly compared IR and machine learning methods for bug triage [20]. They summarized over seventy related articles to discuss the pros and cons of IR and machine learning methods. Through experiments on multiple open-source projects, they found that IR-based bug triage methods had better performance. However, compared to our work, this study is relatively old and does not include recent developments.