# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 5 Works Based on Bug Reports
### Machine Learning Approaches for Deduplication and Triage
The model employs topic clustering using vector topic information combined with self-attention, and the remaining information is utilized for deduplication using conditional attention. Experimental results showed F1 scores of 0.95 and 0.88 for deduplication in Firefox and JDT, respectively. [PERSON] et al. used machine learning models to classify bug reports based on Ericsson's bug data [99]. They extracted text features from bug reports using the TF-IDF method, obtained categorical features using one-hot encoding, and collected log features such as alarms and crash dumps. Bug triage was performed using L2-regularized logistic regression with the Liblinear solver. Experimental results showed precision and recall rates of 89.75% and 90.17%, respectively, with a confidence level of 90%. [PERSON] et al. proposed T-REC [100], which combines machine learning and information retrieval (IR) techniques. After preprocessing and normalizing text information, unstructured natural language descriptions and structured software engineering data are obtained. The vector space model (VSM) is used to convert text into 300-dimensional vector representations, and machine learning ranking methods are employed to rank the technical groups. T-REC also uses a series of BM25F-based extension versions for detection. Finally, a noisy-or classifier is used to calculate the joint probability distribution and provide the final recommendations. Experimental results showed an accuracy of 76.1% for ACC@5, 83.6% for ACC@10, and 89.7% for ACC@20. [PERSON] et al. used a CNN-based method for bug triage [101]. This approach involved converting the content of bug reports into vector representations using word2 vec. By combining developer activity information, a CNN was utilized to map bug reports and developers. Experimental results showed a top-10 accuracy of 0.7489. [PERSON] et al. proposed using backpropagation techniques to improve the accuracy of multiple LDA (latent Dirichlet allocation) [11]. They first applied LDA to bug reports for topic modeling and classification, obtaining the union topic set (UTS). They then performed modeling and classification on priority and severity to obtain the partial topic set (PTS), allowing for the identification of cases where priority or severity was not correctly captured in the classification. Bug reports that were misclassified were analyzed to extract the feature topic set (FTS), and based on FTS information, the bug reports were retriaged to obtain an updated UTS. Experimental results showed that the classification accuracy for different priorities exceeded 84%. [PERSON] et al. proposed HINDBR, a neural network for detecting semantic similarity in bug reports [102]. It transforms the structured or unstructured semantic relationships in bug reports into low-dimensional vector representations and determines whether reports are duplicates based on the distance between vectors in the latent space. [PERSON] et al. argued that directly assigning bug triage to a specific developer in the industry is not reasonable due to frequent personnel changes [103]. Therefore, they proposed using components as the target for triage and recommending the current active developer associated with the component. The process of this method is similar to typical machine learning approaches. Bug reports are preprocessed to remove irrelevant information; then, LDA is used to extract topic features. Finally, a DNN model is constructed to predict the relationship between topic features and corresponding labels. Experimental results showed RR@5 rates of 85.1%, 70.1%, and 92.1% for recommending active developers on the JDT, Platform, and Firefox datasets, respectively. [PERSON] et al. employed an NLP approach using word2 vec to transform the text in bug reports into bag-of-words (BOW) and extract feature vectors [104]. LSTM was trained for bug triage. Experimental results showed that this approach achieved approximately 78% accuracy after six iterations. [PERSON] et al.