Datasets:
ArXiv:
License:
license: mit | |
Programminglanguage: "Python" | |
version: "N/A" | |
Date: "Codesearchnet(Jun 2020 - paper release date)" | |
Contaminated: "Very Likely" | |
Size: "Standard Tokenizer (TreeSitter)" | |
### Dataset is imported from CodeXGLUE and pre-processed using their script. | |
# Where to find in Semeru: | |
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/text-to-code/codesearchnet/python in Semeru | |
# CodeXGLUE -- Code Search (AdvTest) | |
## Task Definition | |
Given a natural language, the task is to search source code that matches the natural language. To test the generalization ability of a model, function names and variables in test sets are replaced by special tokens. | |
## Dataset | |
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following: | |
- Remove examples that codes cannot be parsed into an abstract syntax tree. | |
- Remove examples that #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. | |
Besides, to test the generalization ability of a model, function names and variables in test sets are replaced by special tokens. | |
### Data Format | |
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl | |
For each file, each line in the uncompressed file represents one function. One row is illustrated below. | |
- **repo:** the owner/repo | |
- **path:** the full path to the original file | |
- **func_name:** the function or method name | |
- **original_string:** the raw string before tokenization or parsing | |
- **language:** the programming language | |
- **code/function:** the part of the `original_string` that is code | |
- **code_tokens/function_tokens:** tokenized version of `code` | |
- **docstring:** the top-level comment or docstring, if it exists in the original string | |
- **docstring_tokens:** tokenized version of `docstring` | |
- **url:** the url for the example (identify natural language) | |
- **idx**: the index of code (identify code) | |
### Data Statistics | |
Data statistics of the dataset are shown in the below table: | |
| | #Examples | | |
| ----- | :-------: | | |
| Train | 251,820 | | |
| Dev | 9,604 | | |
| Test | 19,210 | | |
### Example | |
Given a text-code file evaluator/test.jsonl: | |
```json | |
{"url": "url0", "docstring": "doc0","function": "fun0", "idx": 10} | |
{"url": "url1", "docstring": "doc1","function": "fun1", "idx": 11} | |
{"url": "url2", "docstring": "doc2","function": "fun2", "idx": 12} | |
{"url": "url3", "docstring": "doc3","function": "fun3", "idx": 13} | |
{"url": "url4", "docstring": "doc4","function": "fun4", "idx": 14} | |
``` | |
### Input Predictions | |
For each url for natural language, descending sort candidate codes and return their idx in order. For example: | |
```json | |
{"url": "url0", "answers": [10,11,12,13,14]} | |
{"url": "url1", "answers": [10,12,11,13,14]} | |
{"url": "url2", "answers": [13,11,12,10,14]} | |
{"url": "url3", "answers": [10,14,12,13,11]} | |
{"url": "url4", "answers": [10,11,12,13,14]} | |
``` | |
## Reference | |
<pre><code>@article{husain2019codesearchnet, | |
title={Codesearchnet challenge: Evaluating the state of semantic code search}, | |
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, | |
journal={arXiv preprint arXiv:1909.09436}, | |
year={2019} | |
}</code></pre> |