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--- |
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annotations_creators: |
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- machine-generated |
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language_creators: |
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- found |
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languages: |
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- code |
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licenses: |
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- 0bsd |
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- mit |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 100K<n<1M |
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source_datasets: |
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- original |
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task_categories: |
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- sequence-modeling |
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task_ids: |
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- sequence-modeling-code-modeling |
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--- |
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# Dataset Card for [py_ast] |
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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-instances) |
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- [Data Splits](#data-instances) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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- **homepage**: [py150](https://www.sri.inf.ethz.ch/py150) |
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- **Paper**: [Probabilistic Model for Code with Decision Trees](https://dl.acm.org/doi/10.1145/3022671.2984041) |
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- **Leaderboard:** |
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- **Point of Contact:** |
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### Dataset Summary |
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The dataset consists of parsed Parsed ASTs that were used to train and evaluate the DeepSyn tool. |
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The Python programs are collected from GitHub repositories |
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by removing duplicate files, removing project forks (copy of another existing repository) |
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,keeping only programs that parse and have at most 30'000 nodes in the AST and |
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we aim to remove obfuscated files |
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### Supported Tasks and Leaderboards |
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Code Representation, Unsupervised Learning |
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### Languages |
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Python |
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## Dataset Structure |
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### Data Instances |
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A typical datapoint contains an AST of a python program, parsed. |
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The main key is `ast` wherein every program's AST is stored. |
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Each children would have, |
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`type` which will formulate the type of the node. |
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`children` which enumerates if a given node has children(non-empty list). |
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`value`, if the given node has any hardcoded value(else "N/A"). |
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An example would be, |
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''' |
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[ {"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"} ] |
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''' |
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### Data Fields |
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- `ast`: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree. |
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- `type`: explains the type of the node. |
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- `children`: list of nodes which are children under the given |
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- `value`: hardcoded value, if the node holds an hardcoded value. |
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### Data Splits |
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The data is split into a training and test set. |
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The final split sizes are as follow: |
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| | Tain | Valid | |
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| ----- | ------ | ----- | |
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| py_ast examples| 100000 | 50000 | |
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## Dataset Creation |
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[More Information Needed] |
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### Curation Rationale |
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[More Information Needed] |
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### Source Data |
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#### Initial Data Collection and Normalization |
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[More Information Needed] |
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#### Who are the source language producers? |
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[More Information Needed] |
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### Annotations |
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#### Annotation process |
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[More Information Needed] |
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#### Who are the annotators? |
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[More Information Needed] |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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Raychev, V., Bielik, P., and Vechev, M |
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### Licensing Information |
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MIT, BSD and Apache |
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### Citation Information |
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@InProceedings{OOPSLA ’16, ACM, |
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title = {Probabilistic Model for Code with Decision Trees.}, |
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authors={Raychev, V., Bielik, P., and Vechev, M.}, |
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year={2016} |
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} |
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