PyGress

File description

requirement.txt

• These libraries in this file are needed to install for conducting this project.

PyDriller_ExtractData.py

• Code for extracting GitHub Repo data using PyDriller.
• You need to include Project GitHub Repo URL and Project Name in the DataPyPI.csv.

• There are 2 sets of output: GitHub Repo Data and Code before and after commit.
  • GitHub Repo Data can be found in PythonCommits_data/{ProjectName}_data.csv (1 row per 1 Commit).
  • Code before and after commit can be found in the directory PythonFiles/{ProjectName} -> Inside directories from here are grouped code from the same commit author's email (the author's email is hashed as the directory name).

AnalyzeCompetencyScore.py

• Main process for converting code from Project GitHub repo to PyCEFR Competency Score, which is divided into 7 steps.
  • Step 1: Clone Repo PyCEFR
  • Step 2: Create Directory CompetencyScore to store Competency Score from PyCEFR
  • Step 3: Create file filtered_all_projects.csv
  • Step 4: Change Dir to PyCEFR
  • Step 5: Run dict.py to start the Competency Score extraction
  • Step 6: Analyze for tracking the status of each project whether that project is successfully extracted
  • Step 7: Calculate the competency score into sum A1 to C2 from each project

CalculateCompetencyScore.py

• This code is the Step 7 in AnalyzeCompetencyScore.py.

How to run PyGress

Step 0: Clone this repository

• Command script git clone https://github.com/MUICT-SERU/SP2023-Greeedhub.git

Step 1: Install required libraries in requirements.txt

• Command script pip install -r requirements. txt

Step 2: Start Extract GitHub Data from listed repo using code from PyDriller_ExtractData.py

• Start by running the script python PyDriller_ExtractData.py

• Wait until all projects are extracted and stored in directories

• The result you will get from this step is code after and before commits from all projects in the directory PythonFiles/{ProjectName}

• Another result is the GitHub data in directory PythonCommits_data/{ProjectName}.csv (Metadata is coming soon)

  --- Take a big break until all projects are extracted ---

Step 3: Analyze GitHub Data and convert it to the Competency Score list

• Running the code AnalyzeCompetencyScore.py

• Wait until the Competency Score stores in the directory.

• The result you will get from this step is the list of all competency scores in each project in the directory CompetencyScore/{ProjectName}_CompetencyScore.csv (Metadata is coming soon)

• Another result is the grouped list of competency scores (grouped by competency score A1 to C2 as 6 grouped lists), which is in the directory นั่นสิ ลืมแล้ว

  --- Take another huge break until the competency score from all projects are calculated ---

How to run the visualization

1. step 1
2. step 2
3. step 3

Dataset

The dataset is now available in Google Drive [Data/Project_CompetencyScore].