# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 5 Works Based on Bug Reports
### Information Retrieval Approaches for Deduplication and Triage
The neighborhood overlap of nodes is used to enhance the feature analysis of bug reports. Finally, graph classification is used for triage. Experimental results showed improvements in accuracy, precision, recall, and F measure compared to term frequency inverse document frequency (RFSTF) and CNN methods when classifying bugs of different priorities. For example, for bugs with priority p1, the accuracy, precision, recall, and F measure were 0.732, 0.871, 0.732, and 0.796, respectively. [PERSON] et al. proposed a feature extraction model to aid in bug triage deduplication [12]. The model aggregates various features extracted from bug reports, including multiple text features extracted using TF-IDF, time features, context features, and classification features. The authors also proposed a heuristic feature efficiency detection method for evaluation. Experimental results showed that using the extracted features improved deduplication accuracy, recall rate, and F measure by 2%, 4.5%, and 5.9%, respectively. [PERSON] et al. addressed discrete and non-discrete features in bug reports using different approaches [77]. They employed principal component analysis (PCA) for discrete features, specifically using the one-hot encoded method for analysis. For non-discrete features, they transformed the text into a bag-of-words (BOW) representation and used an entropy-based keyword extraction method (greedy variant) for analysis, defining an entropy threshold to filter features. They combined developer contributions and used multiple classifiers to calculate the probability of assigning a bug to a specific developer or team. Experimental results showed that random forest achieved an accuracy of 79% for top-1, 87% for top-5, and 90% for top-10 team assignments. For developer assignments, the accuracy was 54% for top-1, 63% for top-5, and 67% for top-10. [PERSON] et al. pointed out that many bug reports contain ambiguous descriptions and excessive terminology [27], leading to classification hesitation. Traditional machine learning or precise information retrieval methods may not be effective in such cases. Therefore, they proposed an approach based on intuitionistic fuzzy sets (IFS) for classification in the presence of uncertainty. The work included two subparts: IFSDTR and IFSDCR. IFSDTR focuses on the relationship between developers and terminology, while IFSDCR aims to assign multiple fixers to multiple bugs. Experimental results showed that the accuracy of the IFSDTR technique was 0.90, 0.89, and 0.87 for the Eclipse, Mozilla, and NetBeans datasets, respectively. The accuracy of IFSDCR was even higher for the Eclipse, Mozilla, and NetBeans datasets, reaching 0.93, 0.90, and 0.88, respectively. [PERSON] et al. proposed a quality-based classifier that categorizes bugs into six types based on the ISO 25010 standard [57], including reliability, maintainability, usability, and others. They combined multiple feature extraction tools, such as TF-IDF, chi-square, and random trees. Experimental results showed that using TF-IDF + chi-square with random forest yielded the best results in terms of accuracy (76%), recall (70%), and F1 score (70%) for the triage of bugs related to quality. However, the performance was not as good when triaging bugs related to functionality. [PERSON] et al. proposed using fuzzy logic, specifically intuitionistic fuzzy sets (IFS), for bug triage [26]. They first applied LDA to the bugs to generate topic models and classify them into several classes. They then used the Sugeno complement generator to estimate the association value between developers and bug models. Finally, they used IFSim to calculate the similarity between bugs and developers. Experimental results for the Eclipse dataset showed an accuracy of 0.894, precision of 0.897, recall of 0.893, and F measure of 0.896. [PERSON] et al. proposed TM-FBT (topic modeling-based fuzzy bug triaging) [78], which combines topic modeling and fuzzy logic for bug triage.