Teddy: Automatic Recommendation of Pythonic Idiom Usage For Pull-Based Software Projects

A repo for the Teddy tool.

Required Components

• Elasticsearch 2.2.0
• Apache Maven 3.6.3 or higher

Setting up Elasticsearch 2.2.0

1. In .\config\elasticsearch.yml file of Elasticsearch folder, add index.query.bool.max_clause_count: 20480 and action.auto_create_index: .marvel-* to the file. Save and close the file.

2. Using cmd or PowerShell Go to .\bin directory of your local Elasticsearch folder.

3. Execute .\elasticsearch command to run an Elasticsearch instance.

Setting up Teddy

1. Use git clone to make a local copy of this branch on your machine, or download this branch as a .zip package and extract it.

2. From .\config\elasticsearch.yml file in the Elasticsearch folder, check the value of the parameter cluster.name, then set the value of the property cluster inside .\webapp\config.properties in SP2019-TEDDY folder to be the same name.

3. In config.properties file, be sure to set the value of property index to be the name of an existing index in your local Elasticsearch. (To see the list of index in your Elasticsearch, go to localhost:9200/_cat/indices?v=pretty on a web browser).

4. Using another cmd or PowerShell instance, go to .\webbapp directory of SP2019-TEDDY folder

5. Use the command mvn spring-boot:run to launch Spring-boot framework and run TEDDY

6. On any web browser, go to localhost:8080/search to start using TEDDY (Google Chrome is recommended).

PREVENTION MODE USER INSTALLATION

** DON - Please work on this part **

DETECTION MODE USER INSTRUCTION (directly via Linux terminal)

**WARNING: These steps have only been tested on Linux **

DO NOT REMOVE ANY OF THE FILES IN THIS FOLDER

0. Needed dependencies and library before start using:
   • Java JDK 1.8 or above
   • Python 3.X.X
   • pandas library for Python
   • bokeh library for Python
   • git bash for Linux
1. Open a terminal and turn on Elasticsearch server (./elasticsearch) at ~/elasticsearch-2.2.0-linux/bin
2. Make sure there is no any folder located in the directory ~/detection-mode/
3. Open another terminal and execute AutoRun.sh at ~/detection-mode/ with the following order of arguments:
   • .git URL of a GitHub repository
   • directory for the result CSV files from Siamese to be saved, NOT within THE REPO (absolute path recommended)
   • name for the Elasticsearch index of the repo (no whitespace allowed)
   • Example: $ ./AutoRun https://github.com/pallets/flask.git /home/flask-results flask
4. Finally, a file named myplot.html will be created here. This is the final visualization of detection mode. Move it to wherever you like. Use a web browswer (Google Chrome is recommended) to view the file.
5. NOTE: Detection mode takes a considerable amount of time to complete depending on the repository's size and number of commits present.