metadata

task_categories:
  - summarization
  - text-generation
language:
  - en
tags:
  - code
size_categories:
  - 10K<n<100K

Overview

This dataset contains 25,000+ rows of code-docstring-ast data along with additional metadata. Data was gathered from various Python libraries and frameworks and their publicly available GitHub repos. This dataset was created for the purpose of training the CodeT5+ transformer on AST-enhanced code-to-doc tasks.

Sources

The dataset was gathered from various GitHub repos sampled from this repo by Vinta.

The 26 repos are:

matplotlib
pytorch
cryptography
django
prospector
scikit-learn
pandas
numpy
uvicorn
feincms
algorithms
scrapy
authlib
seaborn
coconut
tensorflow
flexx
salmon
mongo-python-driver
virtualenv
sphinx
schema
kornia
scipy
cherrypy
pygame

Sampling was at random; I simply browsed through each category from Vinta's list and chose one from a random interesting category.

Dataset Instance

An instance of the dataset is as follows:

{
  <library> : <The library from which the source code came from>,
  <name> : <The name of the function/class/method>,
  <source_code> : <The raw source code itself stripped of its docstrings and comments>,
  <docstring> : <The corresponding docstring of the code>,
  <type> : <Whether it's a function, method, or class>,
  <file_path> : <The relative path of the file containing the function>,
  <ast_sequence> : <The ast sequence of the raw source code. Scroll down for more info about this>
}

The AST Sequence

A function recursively converts the AST tree into a linear sequence. It uses depth markers (├1>, └2>, etc.) to show parent-child relationships. It also adds node identifiers by pairing each node type with a meaningful identifier. Furthermore, pruning is also applied to irrelevant and shallow identifiers to denoise the dataset.

Here's an example of how the AST sequence is generated:

Example Code

def calculate_area(radius):
    """
    Calculate the area of a circle.
    
    Parameters:
        radius (float): The radius of the circle
        
    Returns:
        float: The area of the circle
    """
    PI = 3.14159
    area = PI * radius * radius
    return area

Resulting AST Sequence

FunctionDef:calculate_area
├1> args:[radius]
├1> Assign:PI
│ └2> Constant:
├1> Assign:area
│ └2> BinOp:
│   ├3> BinOp:
│   │ ├4> Name:PI
│   │ └4> Name:radius
│   └3> Name:radius
└1> Return:
  └2> Name:area

The code is parsed via Python's ast module
A method traverses this tree and linearizes the sequence
Each node is then converted to a string with type-identifier keys
Structural relationships are preserved using the depth markers
Denoising of irrelevant and shallow nodes are applied

Preprocessing

The dataset generally follows CodeBERT's code2nl dataset cleaning standards which are as follows:

Removed comments from the code
Removed examples where code cannot be parsed into an AST
Remove examples that codes cannot be parsed into an abstract syntax tree.
Remove examples where the number of tokens of documents is < 3 or >256
Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
Remove examples that documents are not English

Furthermore, the following cleaning steps specific to this dataset were applied:

Docstrings from source codes was stripped
Standard text cleaning procedures (normalized whitespaces and special characters)
Removed abnormally long AST sequences (>100)
Structural pruning of the AST sequence (removing irrelevant or shallow identifiers/nodes)

Final Statistics

Final Count : 25,489

Average Docstring Length : 51

Average Source Code Length : 591

Average AST Sequence Length : 72

Type Distribution

Methods: 13,149(52.1%)

Functions: 8,671(34.3%)

Classes: 3,444(13.6%)

Major Contributors

TensorFlow (5,455)

PyTorch (5,448)

Matplotlib (2,190)

Django (2,138)

Scipy (1,715)

NOTE

This dataset is imperfect. Due to the varying degrees of code complexity, some fields are inaccurate representations.