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--- |
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license: mit |
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Programminglanguage: "Python" |
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version: "N/A" |
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Date: "Codesearchnet(Jun 2020 - paper release date)" |
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Contaminated: "Very Likely" |
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Size: "Standard Tokenizer (TreeSitter)" |
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--- |
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### Dataset is imported from CodeXGLUE and pre-processed using their script. |
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# Where to find in Semeru: |
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The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/text-to-code/codesearchnet/python in Semeru |
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# CodeXGLUE -- Code Search (AdvTest) |
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## Task Definition |
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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. |
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## Dataset |
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The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following: |
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- Remove examples that codes cannot be parsed into an abstract syntax tree. |
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- Remove examples that #tokens of documents is < 3 or >256 |
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- Remove examples that documents contain special tokens (e.g. <img ...> or https:...) |
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- Remove examples that documents are not English. |
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Besides, to test the generalization ability of a model, function names and variables in test sets are replaced by special tokens. |
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### Data Format |
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After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl |
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For each file, each line in the uncompressed file represents one function. One row is illustrated below. |
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- **repo:** the owner/repo |
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- **path:** the full path to the original file |
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- **func_name:** the function or method name |
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- **original_string:** the raw string before tokenization or parsing |
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- **language:** the programming language |
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- **code/function:** the part of the `original_string` that is code |
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- **code_tokens/function_tokens:** tokenized version of `code` |
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- **docstring:** the top-level comment or docstring, if it exists in the original string |
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- **docstring_tokens:** tokenized version of `docstring` |
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- **url:** the url for the example (identify natural language) |
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- **idx**: the index of code (identify code) |
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### Data Statistics |
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Data statistics of the dataset are shown in the below table: |
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| | #Examples | |
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| ----- | :-------: | |
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| Train | 251,820 | |
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| Dev | 9,604 | |
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| Test | 19,210 | |
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### Example |
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Given a text-code file evaluator/test.jsonl: |
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```json |
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{"url": "url0", "docstring": "doc0","function": "fun0", "idx": 10} |
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{"url": "url1", "docstring": "doc1","function": "fun1", "idx": 11} |
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{"url": "url2", "docstring": "doc2","function": "fun2", "idx": 12} |
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{"url": "url3", "docstring": "doc3","function": "fun3", "idx": 13} |
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{"url": "url4", "docstring": "doc4","function": "fun4", "idx": 14} |
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``` |
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### Input Predictions |
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For each url for natural language, descending sort candidate codes and return their idx in order. For example: |
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```json |
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{"url": "url0", "answers": [10,11,12,13,14]} |
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{"url": "url1", "answers": [10,12,11,13,14]} |
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{"url": "url2", "answers": [13,11,12,10,14]} |
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{"url": "url3", "answers": [10,14,12,13,11]} |
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{"url": "url4", "answers": [10,11,12,13,14]} |
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``` |
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## Reference |
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<pre><code>@article{husain2019codesearchnet, |
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title={Codesearchnet challenge: Evaluating the state of semantic code search}, |
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author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, |
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journal={arXiv preprint arXiv:1909.09436}, |
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year={2019} |
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}</code></pre> |
