| | --- |
| | 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> |
