# 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  
Some work uses IR techniques to enhance the ability to automatically flag bugs. For example, the work of [PERSON] et al. [56] can enhance the quality of the bug report by marking the valuable terms in the bug report. Test results showed that the marking accuracy exceeds 70%, which is very beneficial to the deduplication and triage of the bug. Figure 17 illustrates the process of information-retrieval-based approaches in bug triage. This type of work commonly begins by applying preset rules to preprocess bug reports. These rules include removal of stop words and topic-irrelevant words, splitting and tokenization of the contents, and normalization of the text. This preprocessing step aims to clean and prepare the bug reports for further analysis [57]. After preprocessing, the next step involves extracting useful knowledge from various information sources, such as bug properties, stack traces, and others. These sources provide additional context and relevant information about the bugs. Feature extraction techniques are then applied to capture the essential characteristics or patterns from the extracted information. Finally, similarity measures such as the Jaccard coefficient, cosine distance, or other similarity metrics are used to assess the similarity or correlation between bug reports. These measures compare the feature vectors representing the bug reports and determine their similarity or relatedness. [PERSON] et al. found that duplicate bug reports exhibit a certain degree of temporal locality, which can be leveraged to reduce the search space of duplicate reports and improve the search functionality of bug tracking systems (BTS) [23]. [PERSON] et al. proposed FactorLCS [21], which suggests that if two bug reports share the longest common subsequence of frequently occurring words in the same order, they are more likely to be similar. Based on this idea, they detected duplicate bug reports by matching the sequences. The authors introduced match size within group weight (MSWGW) to weigh the scores of the longest common subsequence (LCS) and obtain the final matching score, aiming to reduce the rate of false matches. [PERSON] et al. proposed a fusion framework based on multilabel classification to categorize bug reports into different groups [22]. The authors trained a multilabel classification model using the MULAN module in the Weka machine learning software package. For a given bug report to be classified, the framework retrieved the top 20 potential similar reports by combining the highest scores from multiple labels. [PERSON] et al. proposed a time-based model based on BM25 Fext to model the submission time and version information of bug reports [58]. This model combined the textual information, category information, and time features to rank potentially similar bug reports. [PERSON] et al. proposed an approach to improve bug management using correlations in crash reports [59]. They determined that if the same bug corresponds to different crashes under different scenarios, these crashes can be correlated. They introduced five rules to determine whether crashes  
Figure 17: General process of information-retrieval-based work. are correlated, including three based on stack trace signatures, one based on temporal locality, and one based on the textual similarity of crash comments. They then used crash correlation to identify duplicate bugs. Experimental results showed that the deduplication method achieved a recall rate of 50% for the Firefox dataset and 47% for the Eclipse dataset, with precision rates of 55% and 35%, respectively. [PERSON] et al. conducted experiments and found that the effectiveness of previous bug report duplication detection methods was overestimated by 17% to 42%. As a result, they suggested using the resolution field extracted from bug reports (e.g., the \"FIXED\" or \"WONTFIX\" tags) to improve the efficiency of duplication detection [60]. [PERSON] et al. developed a system for evaluating the similarity of documents [61].