## 1 Introduction  
Thanks to the increasing capabilities of open source software (OSS) development tools, the number of open-source users and projects is growing each year. Existing software collaboration platforms include millions of developers with different characters and skill sets, as well as a wide variety of projects that offer solutions to various problems. GitHub, is the largest one among these platforms, that hosting more than 40 million repositories to which over 100 million developers have contributed. On this platform, several features are used to manage the distributed and open-source projects of which the most widely used are issues, commits, and pull requests (PRs). Activities related to these features--such as opening an issue, merging a PR, or commenting on a commit--can provide information about the developers and projects. The knowledge obtained from this information can then be used in the form of developer metrics to solve various software engineering challenges.  
The some of the developer metrics used are the number of lines of code in a project, developer degrees of connection to one another, past experience, or common features (e.g., nationality, location, occupation, gender, previously used programming languages). These metrics offer solutions to different problems within OSS development and distributed coding, including automatic assignments (task, issue, bug, or reviewer) [1; 2; 3], project recommendation systems [4; 5], and software defect detection [6].  
However, on platforms with such a large amount of data, it is challenging for developers to find similar projects to their own, identify projects of interest, and reach projects to which they can contribute. As developers primarily use search engines or in-platform search menus to find projects, the constraints of text-based search [7] and challenges related to finding the correct keywords also lead them to miss some projects [8]. While various project recommendation systems are being developed to overcome this problem, projects must be rated by users if recommendation models are to work properly. In the same way that viewers give ratings to movies that they have watched, developers need to rate the projects in which they are interested. However, this does not currently occur on software collaboration platforms, meaning there is no labeled dataset for work on this problem. For this reason, several developer metrics that can be extracted from the activity or features of both developers and projects can be used to calculate the score that a user gives projects.  
In this study, new developer metrics are presented to be used for a variety of open source distributed software development challenges. We developed a project recommendation system due to evaluate these metrics using data from GitHub with the aim of making recommendations to developers based on their GitHub activities. Moreover, to address the sparsity problem on GitHub, we selected a dataset with a high project-user ratio. Despite this handicap, we obtained remarkable results in comparison with a similar study [5]. We present the following research questions for this study:  
1. Can we offer new evaluation methods for GitHub project recommendation problem?
2. Can we use the activities of GitHub users as a developer metric individually?
3. Can new metrics be obtained by combining similar properties or activities?
4. Is a user's amount of activity important in the context of developer metrics?  
In light of these research questions, we organized this paper as follows. In the background section, we discuss the literature on previously proposed developer metrics that have been used for a wide range of OSS development challenges. In the following section, we describe our dataset, proposed metrics, and project recommendation model. We evaluate the metrics using different approaches and methods in the last section.