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Dataset Card for [py_ast]
Dataset Summary
The dataset consists of parsed ASTs that were used to train and evaluate the DeepSyn tool. The Python programs are collected from GitHub repositories by removing duplicate files, removing project forks (copy of another existing repository), keeping only programs that parse and have at most 30'000 nodes in the AST and we aim to remove obfuscated files
Supported Tasks and Leaderboards
Code Representation, Unsupervised Learning
Languages
Python
Dataset Structure
Data Instances
A typical datapoint contains an AST of a python program, parsed.
The main key is ast wherein every program's AST is stored.
Each children would have,type which will formulate the type of the node.
children which enumerates if a given node has children(non-empty list).
value, if the given node has any hardcoded value(else "N/A").
An example would be,
'''
[ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"}, {"type":"Print","children":[5]}, {"type":"BinOpAdd","children":[6,7]}, {"type":"NameLoad","value":"x"}, {"type":"Num","value":"1"} ]
'''
Data Fields
ast: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree.type: explains the type of the node.children: list of nodes which are children under the givenvalue: hardcoded value, if the node holds an hardcoded value.
Data Splits
The data is split into a training and test set.
The final split sizes are as follows:
| train | validation | |
|---|---|---|
| py_ast examples | 100000 | 50000 |
Dataset Creation
[More Information Needed]
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Raychev, V., Bielik, P., and Vechev, M
Licensing Information
MIT, BSD and Apache
Citation Information
@InProceedings{OOPSLA ’16, ACM, title = {Probabilistic Model for Code with Decision Trees.}, authors={Raychev, V., Bielik, P., and Vechev, M.}, year={2016} }
@inproceedings{10.1145/2983990.2984041,
author = {Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
title = {Probabilistic Model for Code with Decision Trees},
year = {2016},
isbn = {9781450344449},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2983990.2984041},
doi = {10.1145/2983990.2984041},
booktitle = {Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications},
pages = {731–747},
numpages = {17},
keywords = {Code Completion, Decision Trees, Probabilistic Models of Code},
location = {Amsterdam, Netherlands},
series = {OOPSLA 2016}
}
Contributions
Thanks to @reshinthadithyan for adding this dataset.
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