Intelligent GitHub Link Error Analyzer with Structural Pattern Matching

Project Code :TCMAFS1384

Objective

The main objective of this project is to develop an intelligent system which automatically analyses GitHub repository links to identify structural problems in software projects. The system will use GitHub API to obtain repository data which includes directory structure and README file content. Another objective is to apply structural pattern matching techniques to identify important components like source folders, test directories, and configuration files. The project also aims to evaluate the quality of repository documentation by analyzing README files for essential information such as installation instructions, external links, and license references. The system produces an automated analysis report which assigns a repository quality score according to established evaluation standards. The system allows users to quickly assess both the dependability and functionality of a repository.

Abstract

The rapid growth of open-source software has led to a significant increase in the sharing of repository links across development platforms. However, many GitHub links contain structural issues such as missing documentation, incomplete project organization, or invalid repository references. This project proposes an Intelligent GitHub Link Error Analyzer with Structural Pattern Matching to automatically evaluate repository quality and detect structural inconsistencies. The system analyses repository links by integrating with the GitHub API to retrieve project metadata, directory structures, and README content. Using pattern matching techniques, it identifies key project components such as source folders, test directories, documentation files, and dependency configurations. The analyser also evaluates README content to detect essential elements like installation instructions, external links, and license references. Based on these checks, the system generates an automated analysis report and assigns a repository quality score. This approach helps developers quickly identify structural issues and improves the reliability and usability of shared repository links. The proposed system demonstrates how automated repository analysis can support better software documentation and maintainability in collaborative development environments.

Keywords: GitHub Repository Analysis, Structural Pattern Matching, Open Source Repository Analytics, Repository Quality Scoring, README Analysis, Repository Quality Assessment, Repository Structure Detection, GitHub Repository Validation.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.