Datasets:

google
/

code_x_glue_cc_code_refinement

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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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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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