## 2 Related Works
### Issue Report Classification  
Researchers proposed machine learning techniques and deep learning techniques for classifying issue reports. [PERSON] et al. [2] and [PERSON] et al. [3] classified issue reports into bug or non-bug using machine learning techniques. [PERSON] et al. [6], [PERSON] et al. [7], [PERSON] et al. [8], [PERSON], [PERSON], et al. [9], and [PERSON] et al. [10] used deep learning techniques for the same task. [PERSON] et al. [2] proposed a two-stage issue report classification approach by combining textual data of an issue report with developer information. In the first stage, the approach extracts semantic perplexity features from an issue report's textual data using a text-based classification model such as support vector machines (SVM), naive Bayes, and logistic regression. In the second stage, the approach classifies issue reports into bug or non-bug by applying logistic regression to the textual data of an issue report and developer information. The two-stage approach showed an average F1-score of 75.21% in the first quartile. [PERSON] et al. [3] proposed an approach to classify issue reports into bug or non-bug using the summary part of an issue report. For the classification, the approach applies machine learning algorithms of naive Bayes, linear discriminant analysis, support vector machine (SVM), and logistic regression. The authors experimented with three open-source projects to evaluate the approach's Precision, Recall, F1-score, and accuracy. The results showed an F1-score ranged from 71.11% to 72.15%. [PERSON] et al. [6] proposed a deep learning-based approach for classifying issue reports into bug or non-bug. The approach collects issue data from different issue tracking systems and pre-processes them. To detect and correct misclassified issues, the approach uses k-nearest neighbor algorithm. Then, the approach labels issues with bug or non-bug tags using an attention-based bi-directional long short-term memory network. To evaluate the approach, the authors used open-source projects such as Apache, JBoss, and Spring framework. Their experimental results showed a micro F1-score of 85.6%. [PERSON] et al. [7] conducted a study that classifies issue reports using existing labeling techniques [11; 12; 13]. [PERSON] et al. [7] proposed TicketTagger, an automated system that assigns suitable labels to issue reports opened on GitHub projects. When a developer posts an issue report on GitHub, TicketTagger assigns one of Bug, Enhancement, or Question to the issue report. TicketTagger achieved an F1-score of 82.3% to 83.1%. [PERSON] et al. [10] proposed a deep-learning model for classifying issue reports into relevant user manuals using CNN and RNN models. They also conducted a comparison experiment on the task of categorizing issues into relevant labels, which resulted in an F1-score of 54.34% for Notepad++ (3 labels), 52.73% for Komodo (2 labels), and 67.14% for VS Code (2 labels). The data used in this study consist of the title and body text of issue reports and user manuals. The study demonstrated that the proposed model effectively classifies issue reports, compared to the state-of-the-art approach, TicketTagger [7], for issue report classification. [PERSON] et al. [8] proposed a CNN\(-\)based approach for classifying bug reports into Production or Test. The approach uses textual features extracted from source files and bug reports. The approach trains a source file classifier that classifies a source file into a production file or a test file using the name and contents of the source file. Then, the approach fine-tunes the source file classifier to classify a bug report into a production bug report or a test bug report using the title and content of a bug report. Their experimental results showed a macro F1-score of 83.9%. [PERSON], [PERSON], et al. [9] proposed a GitHub issue report classification method named personal information fine-tuning network (PIFNTNet). They utilized transfer learning and the submitter's personal information to classify issue reports into bugs or non-bugs.