Automated Code Discovery and Evaluation for Github Repository

DOI

10.2991/978-94-6463-718-2_119How to use a DOI?

Keywords

Github; Automated Systems; Repositories; Ranking Systems; Code Recommendation

Abstract

We propose a system that will search repositories from GitHub and provide code recommendation based on specific characteristics of repositories in order to help developers/students easily find high-quality code. With millions of open-source projects hosted on GitHub, it’s easy to get lost in the sea of code. It can be difficult for users to determine the most relevant code for their work without taking hours to comb through repository records. Our system works with GitHub’s API and retrieves aside repositories by user preferences in terms of programming language, topics of the projects and popularity among the projects etc. Once we have a compilation of relevant repositories, our system goes down the rabbit hole and analyses each repository through advanced code quality assessment tools. These tools analyze much-needed parameters such as the performance of the code, the readability of the code, maintainability of the code, if it adheres to good coding standards, etc. We also incorporate dynamic performance measurement to assess run-time performance and memory usage, therefore giving you a global assessment of how the code performs It also considers community engagement metrics, such as stars, forks and issue reports. These indicators provide insight into the code’s trustworthiness and real-world application. An amalgamation of these parameters helps our system rank the repositories and suggests the most relevant ones to users. So, if you are a developer or a student that want to find lazy solutions for their projects: You came to the right place!

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Sumathi
AU  - E. Logavignesh
AU  - A. Muhammadu Ali
PY  - 2025
DA  - 2025/05/23
TI  - Automated Code Discovery and Evaluation for Github Repository
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1427
EP  - 1437
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_119
DO  - 10.2991/978-94-6463-718-2_119
ID  - Sumathi2025
ER  -