This repo contains a big dataset (more than 4,000 observations) of public fragments of python code extracted from: Pastebin, Gist and ActiveState. These are used to estimate the most common number of errors of any python file (tau).

This value is used as default tolerance for the Corral Quality Assurance Index (QAI).

Yo can see more about this in the paper: Astronomical Data Processing Through Model-View-Controller Inspired Architecture

• requirements.txt: before running anything, you need to run

    $ pip install -r requirements.txt
  

• corral_qai.py: Data Collector Utility. This file extracts data from a data-source and dumps into a SQLite database.

• metrics.py: Creates a Pandas dataframe from the SQLite database.

• settings.py.template: settings.py template needed to run corral_qai.py

• Makefile: Gnu-Make routines

• analysis.ipynb: the experiment to determine tau.

• The data are distributed in two formats: a CSV data/corral_qai.csv and a SQLite Dump corral_qai.sql

The csv and the sql file attributes are:

• id: internal id (integer unique)
• timestamp: date and time of the observation (datetime)
• response: the full dump of the retrieved data for this observation (text)
• source: gist, pastebin or active state (text)
• source_id: id used internally for the source (text)
• description: a description of the file (text)
• file_name: name of the file (text)
• file_sha512: hash of the file (unique)
• file_raw_url: where to find the original data (text)
• file_size: size in bytes of the file (integer)
• file_content: the full content of the file encoded in utf-8 (text)
• flake8_output: full output of the flake8 analysis over the file (text)
• flake8_errors: number of errors found by flake8 (integer)

If you are interested in using this dataset in your research please cite us as

> foo fo fo

Or in bibtext

All information here is made public by their authors.