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  The Data Collection and Preprocessing for this Hugging Face dataset involved two main steps.
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  First, GitHub repositories known for high code quality were downloaded and labeled as highly readable. The extracted methods are labeled with a score of 4.5.
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  Second, the code was intentionally manipulated to reduce readability. The resulting code was labelled with a score of 1.5.
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- This resulted in an automatically generated training dataset for source code readability classification.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  The Data Collection and Preprocessing for this Hugging Face dataset involved two main steps.
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  First, GitHub repositories known for high code quality were downloaded and labeled as highly readable. The extracted methods are labeled with a score of 4.5.
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  Second, the code was intentionally manipulated to reduce readability. The resulting code was labelled with a score of 1.5.
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+ This resulted in an automatically generated training dataset for source code readability classification.
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+ #### Who are the source data producers?
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+ The source data producers are the people that wrote the used open source Java projects.
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+ #### Personal and Sensitive Information
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+ The ratings of the code snippets are automatically assigned. Thus, no personal or sensitive information is contained in this dataset.
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+ ## Bias, Risks, and Limitations
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+ The assigned labels are not accurate, as they are only an estimation.
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+ ### Recommendations
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+ The dataset should be used to train Java code readability classifiers. We recommend fine-tuning and evaluation on manually labelled data.
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+ ## Dataset Card Authors
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+ Lukas Krodinger, [Chair of Software Engineering II](https://www.fim.uni-passau.de/en/chair-for-software-engineering-ii), [University of Passau](https://www.uni-passau.de/en/).
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+ ## Dataset Card Contact
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+ Feel free to contact me via [E-Mail](mailto:[email protected]) if you have any questions or remarks.