Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/66680| Title: | GitHub repository analysis & prediction | Authors: | Li, Shing To | Keywords: | DRNTU::Business::International business::Data processing DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications |
Issue Date: | 2016 | Abstract: | GitHub is a popular hosting service for software projects boasting over 35 million repositories. Many software projects today rely upon reusing existing Open Source projects in the form of a starting reference or as a package dependency. Bad software dependencies may impact a project in the long run. This project aims to use data mining to uncover patterns and discover new knowledge on what makes a repository healthy. To apply the results of this finding, a web application that uses the results of this analysis has been built and provides prediction for a GitHub repository. This web application can be visited at http://gitvital.ddns.net while the server is online. | URI: | http://hdl.handle.net/10356/66680 | Schools: | School of Computer Engineering | Rights: | Nanyang Technological University | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
| Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FYPSCE15-0446-LiShingTo-U1322698E-FinalReport-Amended.pdf Restricted Access | Amended Final Report | 1.29 MB | Adobe PDF | View/Open |
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