## 3 Problem Methodology and System Design  
### Problem Methodology  
Text information has been presented by [PERSON] et al. [21] as a mechanism for automatically detecting duplicate pull requests on GitHub. They assess how similar the text is to other pull requests already in existence before returning a candidate list of the most comparable ones. Then, this model was tested against three well-known GitHub projects, including Rails, Elastic search, and AngularJS. The results indicated that there is a 55.3% to 71.0% chance of finding duplication based on the combination of title and description similarities. A mechanism for automatically detecting duplicate contributions in the pull-based paradigm [32] has also been devised by the authors, who compared textual and change similarity. They begin by calculating the resemblance between their own work and that of other previously published works. Finally, this method provides a newer contributions list that is mostly similar to the possible duplicate contribution by comparing the change and textual similarities. When textual and change similarity are used together, 83.4% of the duplicates are found on average, compared to 54.8% when textual similarity is used alone and 78.2% when change similarity is used alone, according to simulation data. The issues of duplicate pull requests were further examined in [33] for situations that lead to multiple pull requests and how the integrator preferentially handles multiple pull requests. The sustainability of the various OSS initiatives depends on the continuance of their development communities. Communities can keep developers from leaving and get them to come back if they know how and why developers stop working or take breaks. Duplicate efforts have generally been explored in the literature, including duplicate queries, duplicate pull requests, and duplicate bug reports. Studies have mostly concentrated on outlining the dangers in duplicates and suggesting ways to find and get rid of them. The indiscriminate use of pull requests in OSS repositories can result in a variety of problems, including duplicated, verbose, and incomplete descriptions, which impede the project's growth and upkeep.  
However, it is unclear whether the pull request template has been widely used on GitHub and what effects it might have. Such situations can reduce the effectiveness of cooperative development. Duplicate pull requests result in wasted time and effort, both during the review and update processes that follow submission as well as during the contributors' initial work before submission. Consequently, it is important to help the project team and keep core developers and their knowledge from leaving. However, not much is known about the stages developers go through when they stop working on a project or how they move from one stage to the next. Integrators have the discretion to add new code and accept pull requests in the pull-based development approach. Pull requests should ideally be allocated to integrators and examined as soon as possible after being submitted. However, integrators often have trouble getting through pull requests in a timely manner because there are so many of them in popular projects. Additionally, the analysis of such existing works [21; 32; 33] takes a lot of time, which results in a rising number of pull requests. Therefore, the automatic duplicate detection method is needed to save time and effort in the review process. To solve those problems, the optimal duplicate pull requests detection method is proposed, using hybrid deep learning (HDL-ODPRs). The key objectives of HDL-ODPR method include the following:  
1. To introduce optimal duplicate pull requests detection method to automatically detect duplicates to improve the accuracy.
2. To propose optimization algorithm to extract textual data from pull requests in OSS repositories.
3. To develop hybrid deep learning techniques for the similarity computation (textual, file-change, and code-change similarities) to further enhance the duplicate detection accuracy.