updated report
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@ -21,7 +21,7 @@ expected info, you'll be fine.-->
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The code and result files, part of this submission, can be found at:
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The code and result files, part of this submission, can be found at:
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- Repository: [https://github.com/infoMA2023/project-02-bug-prediction-maggicl](https://github.com/infoMA2023/project-02-bug-prediction-maggicl)
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- Repository: [https://github.com/infoMA2023/project-02-bug-prediction-maggicl](https://github.com/infoMA2023/project-02-bug-prediction-maggicl)
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- Commit ID: **5f30b3b71b50b28f4c870ff114a760ece1005531**
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- Commit ID: **786d6a06847b217bdad53804f8a6c79e30134ef3**
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# Data Pre-Processing
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# Data Pre-Processing
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@ -429,4 +429,4 @@ classifiers would be useful in predicting potential bugs in the Google JSComp pr
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literature presented during lecture, it is not certain if the same trained classifier would yield acceptable results for source
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literature presented during lecture, it is not certain if the same trained classifier would yield acceptable results for source
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code from another project. This is because differences in coding conventions, the bug tracking process, or simply the definition of
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code from another project. This is because differences in coding conventions, the bug tracking process, or simply the definition of
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what constitutes a bug may vary from project to project. A solution to this problem might be to simply train the classifiers on a dataset
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what constitutes a bug may vary from project to project. A solution to this problem might be to simply train the classifiers on a dataset
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coming from the project where bug prediction is needed.
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coming from the project where bug prediction is needed.
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