TRUST AWARE RECOMMENDER SYSTEM FOR SOCIAL CODING PLATFORMS (GITHUB CASE STUDY)

TRUST AWARE RECOMMENDER SYSTEM FOR SOCIAL CODING PLATFORMS (GITHUB CASE STUDY)

Abstract:
Social coding platforms, such as GitHub, have revolutionized the way software developers collaborate and contribute to open-source projects. These platforms host millions of repositories and provide an abundance of software artifacts, making it challenging for developers to discover relevant and trustworthy projects. To address this issue, trust-aware recommender systems have emerged as valuable tools to assist developers in finding high-quality projects that align with their interests and expertise.

In this study, we present a trust-aware recommender system specifically designed for social coding platforms, with a focus on GitHub. The proposed system leverages trust-related information, such as user ratings, reviews, and social connections, to generate personalized recommendations for developers. The key objective is to improve the accuracy and relevance of project recommendations while considering the trustworthiness of the projects and their contributors.

To develop the trust-aware recommender system, we employ a hybrid approach that combines collaborative filtering and trust-based techniques. We utilize collaborative filtering to capture user preferences and similarities, enabling the system to recommend projects based on the behavior and interests of similar developers. Additionally, we integrate trust-based techniques that take into account the reputation and credibility of projects and contributors. By considering trustworthiness, the system aims to mitigate risks associated with low-quality or malicious projects.

To evaluate the effectiveness of the proposed trust-aware recommender system, we conduct experiments using real-world data from GitHub. We compare the system's performance against baseline methods, including traditional collaborative filtering, and assess its ability to generate accurate and trustworthy recommendations. We measure the system's performance using standard evaluation metrics, such as precision, recall, and F1-score.

The results demonstrate that the trust-aware recommender system outperforms traditional collaborative filtering approaches by effectively incorporating trust-related information. The system successfully recommends projects that align with developers' interests while considering the trustworthiness of the projects and contributors. These findings highlight the potential of trust-aware recommender systems in enhancing the discovery and collaboration experience on social coding platforms like GitHub.

Overall, this research contributes to the growing body of work on trust-aware recommendation systems and provides insights into their applicability within the context of social coding platforms. The proposed system offers a valuable tool for developers to discover reliable and relevant projects, fostering collaboration and innovation within the open-source software development community.

TRUST AWARE RECOMMENDER SYSTEM FOR SOCIAL CODING PLATFORMS (GITHUB CASE STUDY). GET MORE  COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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