## 2 Related Work  
To facilitate comprehension of our research, we provide a brief summary of background information and relevant studies. The research gap related to the pull requests duplication detection in OSS is given in Table 1.  
An approach to recommend technical and managerial reviewers for GitHub pull requests is proposed by [PERSON] et al. [22]. Target pull requests are evaluated by utilizing the hybrid recommendation method. They conducted trials on two major GitHub projects and evaluated the strategy with pull requests. Their recommendation model performed better and differentiates participation types well. [PERSON] et al. [23] have performed an ongoing investigation into the effects of multi-reviewing the resolution of pull requests in GitHub open-source projects. Developers have chosen how many pull requests are examined, and examined how a different number of pull requests has varied results. Their simulation findings demonstrated that increased review switching may cause a pull request's resolution time to increase. TF-IDF and a deep learning technique was proposed by [PERSON] et al. [24] (word and document embedding). They take into account the frequent characteristics over the \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs.** & **Year** & **Methodology** & **ML/DL Technique** & **OSS Repositories** & **Remarks** & **Research Gap** \\ \hline
[22] & 2018 & Code review, pull-request prediction & SVM classifier & GitHub: Ruby on Rails and Angulars. & Detection accuracy & Process for real-world issues may take a long time \\
[23] & 2019 & Multi-reviewing, pull-request prediction & Linear regression & Github REST API & Resolution & Not identifying and lacency acquiring related issues \\
[24] & 2020 & Information retrieval, word and doc. embedding & Skip-gram model, PV-DBOW & GitHub:ksic projects (April 2018) & W-score and D-score & Affected by data dimensionality issues \\
[25] & 2019 & Recommendation process, pull-request prediction & Learning-to-rank (LTR) & GitHub: GHTorrent & Hit ratio, mean reciprocal rank & Different personality traits, educational backgrounds, and expertise levels \\
[26] & 2020 & Pull-request prediction & - & GitHub API: Java projects, DABLUE, CLAN & Success rate, Precision & Metadata is curated from different OSS forges \\
[27] & 2020 & Text, web data and code analysis & Portraitmodel & GitHub: TeslaZY & Precision & Not provide sufficient information \\
[28] & 2020 & CTCPPre, pull-request prediction & XGBoostclassifier & GitHub: 28 projects & Accuracy, precision, recall, & Troublesome and time-consuming \\
[29] & 2021 & Pull-request prediction, LTC prediction & Naive Bayes, kNN, decision tree, and random forest & GitHub: REST API and AUC & Not provide high detection rate \\
[30] & 2019 & Pull-request prediction & XGBoostclassifier & GitHub: 8 open source & Accuracy, precision & The complexity of modern distributed software development is rising \\
[31] & 2021 & Bot prediction, Pull-request prediction & Decision trees, Random forest, SVM, logistic regression, kNN & GitHub: ground truth dataset & Precision, recall and F1-score & the existence of bots, which lowers the detection’s precision \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of research gap. whole issue concerning document corpus TF-IDF. The deep significance of various words and various documents is measured, respectively, by word embedding and document embedding. Then, depending on the total score created by combining the three similarity ratings, they suggest connected topics. [PERSON] et al. [25]'s proposal of the Repo Like technique allowed developers to recommend repositories on the basis of the learning-to-rank methodology and linear combination. The personalized suggestion process was carried out by using a learning-to-rank-based methodology.