数据挖掘作业代写 Mining Software Repositories Assignment 

Mining Software Repositories Assignment

数据挖掘作业代写 The Jupyter notebook file included above should be your starting point for this assignment. All questions outlined below

In the assignment, we will focus on basic data collection from GitHub and how to answer direct data-driven questions from that mined data.

This is an individual assignment, and each student is expected to develop their own methods for answering the questions. This type of analysis can be accomplished in multiple ways, so please try to develop an intuition for your chosen methodology.

Problems:   数据挖掘作业代写

FILE: MSR1.ipynb

The Jupyter notebook file included above should be your starting point for this assignment. All questions outlined below are also included in the file. The assignment has two parts, each with their own targets from the GitHub API:

Part 1: discourse/discourse repo

For this part, we will investigate the discourse/discourse (Links to an external site.) project.

• Question 1: What is the total number of unique contributors for this project? (Contributions include commits and pull requests)


• Question 2: Which user made the most contributions to the project?


• Question 3: Which user made the most commits to the discourse/app/models/badge.rb(Links to an external site.) file?

Part 2: Dan Abramov (gaearon)

For this part, we will investigate Dan Abramov (Links to an external site.) (prominent developer of React (Links to an external site.), co-author of Redux (Links to an external site.) and Create React App (Links to an external site.)).

• Question 1: Which of Dan's projects did he contribute to most often in the past three years? (From June 1, 2017 to June 1, 2020)


• Question 2: The React(Links to an external site.) project was founded in 2013. When did Dan first make a release in the facebook/react (Links to an external site.) project? And what was the version number of that release?

What to hand in:  数据挖掘作业代写

Report (filename: [onid-username]-msr1.pdf)

• Detail your methodology and results for each question, and include citations for any references/resources used.


• Write in an academic tone similar to the research papers assigned in the course. Be precise and write only as much as needed to convey your results.


• Any format is allowed, but if possible follow the ACM(Links to an external site.) or IEEE (Links to an external site.) formats since it will provide you with practice.


• Do not simply include code to demonstrate your methods (those implementation details belong in the notebook file).

Jupyter Notebook (filename: [onid-username]-msr1.ipynb)

• Include all data extraction and analysis code used to answer each question.


• Scrub any personal or confidential information (e.g. authentication tokens) from the file.


• Do not hard-code data into your notebook, but instead include your commands for interacting with the GitHub API.

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