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Python-based analysis of the 30 most-starred GitHub projects across the 15 most popular programming languages (TIOBE). The study focuses on uncovering the origins, growth patterns, and maintenance strategies of colossal files within these projects.

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Colossal-Files-Research

πŸ“Š Analysis of Top GitHub Projects Across Popular Programming Languages

This repository offers a comprehensive analysis of the 30 most-starred GitHub projects across the 15 most popular programming languages, as ranked by the GitHut 2.0. The study delves into the lifecycle of colossal files within these projects, examining their origins, growth trajectories, and maintenance strategies, with a particular focus on their evolution and impact on overall project dynamics.


πŸš€ Project Goals

The objectives of this project are to:

  • Examine repository metadata to identify patterns in highly starred projects.
  • Analyze the lifecycle of colossal files, including their creation, modifications, and role within the project.
  • Evaluate contributor dynamics, focusing on how key maintainers influence project development.
  • Identify trends in codebase size and complexity over time.

πŸ› οΈ Tools and Technologies

This analysis leverages a suite of powerful tools and libraries to extract, process, and visualize data:

  • PyDriller: For extracting repository metadata and commit history.
  • CLoC: For detailed analysis of code structure, including size and complexity.
  • Pandas: For robust data manipulation and statistical analysis.
  • NumPy: For numerical computations and efficient array handling.
  • Matplotlib: For generating insightful visualizations.

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Python-based analysis of the 30 most-starred GitHub projects across the 15 most popular programming languages (TIOBE). The study focuses on uncovering the origins, growth patterns, and maintenance strategies of colossal files within these projects.

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