# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 3 The Roadmap of Existing Literature  
### Overview of Relevant Literature in Recent Years  
Deduplication and triage fundamentally involve considering the similarity between bugs; therefore, the extraction of bug features and the calculation of similarity are the main research topics. As shown in Figure 6, the evolution of techniques used in related works exhibits a clear temporal pattern. Around 2010, traditional text matching and machine learning methods were the mainstream approaches for determining similarity. Traditional text matching methods primarily used dynamic programming-based techniques such as longest common subsequence and longest common substring. Meanwhile, machine learning methods at that time mainly relied on SVM, naive Bayes, and other classification models. Some representative works during this period include [21; 22; 23]. Around 2015, information retrieval methods started to be extensively developed and used, including topic modeling, fuzzing set, and text feature extraction. These methods can more accurately model bug reports, extract feature vectors, and measure the distance between texts using similarity calculation techniques. Some typical works during this period include [24; 25; 26]. After 2015, thanks to the rapid advancement of deep learning methods, especially various neural network models, such as NLP-based models, adjusted deep learning methods showed outstanding effects in large-scale similarity analysis. Other typical neural network models, such as CNNs, have also been applied in feature extraction. Some representative works during this period include [27; 28; 29]. The future development of related techniques may follow two directions. First, combining the strengths of IR and ML for deduplication and triage may be a promising approach. IR excels in accurate feature extraction, while ML outperforms in similarity analysis, recommendation, and prediction. Some works have already proposed methods based on this idea [30; 31]. Secondly, optimizing the application of the latest advances in machine learning and information retrieval, such as transformer models used in large-scale language processing, after appropriate transfer learning may lead to better results than the current \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Cited Paper** & **Study on Commonly Used Methods** & **Study on Runtime Information-Based Approaches** & **Study on Information Retrieval Approaches** & **Study on Machine Learning Approaches** \\ \hline [PERSON] et al. [18] & Not included & Not included & General description of the IR-based methods & General description of the ML-based methods \\ \hline [PERSON] et al. [7] & Not included & Not included & Deduplication using IR methods & Deduplication using NLP, naive Bayes, etc. \\ \hline [PERSON] et al. [19] & Not included & Not included & Not included & Bug triage using six ML models \\ \hline [PERSON] et al. [20] & Not included & Not included & Bug triage using IR methods & Bug triage using ML methods \\ \hline Our work & Feature extraction methods and similarity calculation methods & Deduplication and triage based on runtime stacks, coverage, and context & Deduplication and triage based on texture analysis, topic modeling, etc. & Deduplication and triage based on CNN, LSTM, transformer, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 6: How the state-of-the-art deduplication and triage techniques evolved. models. These potential research directions are discussed in detail in Sections 4 and 5 of this paper. Figure 7 illustrates some of the recent works in the field of deduplication and triage, including approaches based on runtime information, information retrieval methods for bug reports, and machine learning approaches for bug reports. Overall, deduplication and triage have been hot research topics in recent years. In terms of research quantity, the majority of works have focused on deduplication and triage based on bug reports.