Support streaming (#3)

- Skip trying to extract uncompressed files (3589eeae6d0afd429d132ccc287f2f5d8c3137da)
- Delete legacy dataset_infos.json (8a872ddeccbb8e68ae11b30095421fad733a3cf6)
- Update citation information (409f41b1a3a4661cc57197154432c8ee111423cb)

README.md

@@ -209,9 +209,45 @@ Computational Use of Data Agreement (C-UDA) License.
 ### Citation Information
 
 ```
-@article{CodeXGLUE,
-         title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
-         year={2020},}
+@article{DBLP:journals/corr/abs-2102-04664,
+  author    = {Shuai Lu and
+               Daya Guo and
+               Shuo Ren and
+               Junjie Huang and
+               Alexey Svyatkovskiy and
+               Ambrosio Blanco and
+               Colin B. Clement and
+               Dawn Drain and
+               Daxin Jiang and
+               Duyu Tang and
+               Ge Li and
+               Lidong Zhou and
+               Linjun Shou and
+               Long Zhou and
+               Michele Tufano and
+               Ming Gong and
+               Ming Zhou and
+               Nan Duan and
+               Neel Sundaresan and
+               Shao Kun Deng and
+               Shengyu Fu and
+               Shujie Liu},
+  title     = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
+               and Generation},
+  journal   = {CoRR},
+  volume    = {abs/2102.04664},
+  year      = {2021}
+}
+@article{tufano2019empirical,
+  title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},
+  author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
+  journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},
+  volume={28},
+  number={4},
+  pages={1--29},
+  year={2019},
+  publisher={ACM New York, NY, USA}
+}
 ```
 
 ### Contributions

common.py

@@ -47,7 +47,7 @@ class Child:
 
         downloaded_files = {}
         for k, v in urls_to_download.items():
-            downloaded_files[k] = dl_manager.download_and_extract(v)
+            downloaded_files[k] = dl_manager.download(v)
 
         return [
             datasets.SplitGenerator(

dataset_infos.json

@@ -1 +0,0 @@
-{"medium": {"description": "CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement\n\nWe use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length.", "citation": "@article{10.1145/3340544,\nauthor = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\ntitle = {An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation},\nyear = {2019},\nissue_date = {October 2019},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nvolume = {28},\nnumber = {4},\nissn = {1049-331X},\nurl = {https://doi-org.proxy.wm.edu/10.1145/3340544},\ndoi = {10.1145/3340544},\nabstract = {Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation, we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9--50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.},\njournal = {ACM Trans. Softw. Eng. Methodol.},\nmonth = sep,\narticleno = {19},\nnumpages = {29},\nkeywords = {bug-fixes, Neural machine translation}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/code-refinement", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "buggy": {"dtype": "string", "id": null, "_type": "Value"}, "fixed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "fixed", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_cc_code_refinement", "config_name": "medium", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 32614834, "num_examples": 52364, "dataset_name": "code_x_glue_cc_code_refinement"}, "validation": {"name": "validation", "num_bytes": 4086741, "num_examples": 6546, "dataset_name": "code_x_glue_cc_code_refinement"}, "test": {"name": "test", "num_bytes": 4063673, "num_examples": 6545, "dataset_name": "code_x_glue_cc_code_refinement"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/train.buggy-fixed.buggy": {"num_bytes": 16188348, "checksum": "4570731680fa183650864e8729a7354d235c9a3ef42f0085ace3441418074085"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/train.buggy-fixed.fixed": {"num_bytes": 15798070, "checksum": "009c121662602642bc55f6882f220aea6a738e6a11f2c4df86e7fe3cd30c175c"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/valid.buggy-fixed.buggy": {"num_bytes": 2028309, "checksum": "8ad01f88be2009599007f40427458d6d2601fe93f2f1d65b0f46b7d414a3add2"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/valid.buggy-fixed.fixed": {"num_bytes": 1979872, "checksum": "7ef5e4b2e95914e0eceb4f2cf6dfad0641625145319b9836db70d3f8745ad2d6"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/test.buggy-fixed.buggy": {"num_bytes": 2014594, "checksum": "21107528c3b25bfdec24d0c4c18a953de31c26f3795a7d7c9e108a60396bcd38"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data/medium/test.buggy-fixed.fixed": {"num_bytes": 1970531, "checksum": "4b13298647e9a782bf908d4a26710e97a1846f5513a9bf1aa46ac8223fb84b3d"}}, "download_size": 39979724, "post_processing_size": null, "dataset_size": 40765248, "size_in_bytes": 80744972}, "small": {"description": "CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement\n\nWe use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length.", "citation": "@article{10.1145/3340544,\nauthor = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\ntitle = {An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation},\nyear = {2019},\nissue_date = {October 2019},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nvolume = {28},\nnumber = {4},\nissn = {1049-331X},\nurl = {https://doi-org.proxy.wm.edu/10.1145/3340544},\ndoi = {10.1145/3340544},\nabstract = {Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation, we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9--50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.},\njournal = {ACM Trans. Softw. Eng. 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