| --- |
| language: |
| - en |
| tags: |
| - text-classification |
| widget: |
| - text: The app crashed when I opened it this morning. Can you fix this please? |
| example_title: Likely bug report |
| - text: Please add a like button! |
| example_title: Unlikely bug report |
| --- |
| # Model Card for Model ID |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
| This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Developed by:** [More Information Needed] |
| - **Shared by [optional]:** [More Information Needed] |
| - **Model type:** [More Information Needed] |
| - **Language(s) (NLP):** [More Information Needed] |
| - **License:** [More Information Needed] |
| - **Finetuned from model [optional]:** [More Information Needed] |
|
|
| Model Card: Bug Classification Algorithm |
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| Purpose: To classify software bugs according to their clarity, relevance, and readability using a revamped dataset of historical bugs. |
|
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| Model Type: Machine Learning Model (Supervised Learning) |
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| Dataset Information: |
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| Historical Software Bugs Dataset |
| Split into training and validation sets - Training Data consists of approximately 80% of data and validation/testing data comprises of the remaining 20%. |
| Each example contains features including descriptions of software bugs along with human annotations specifying whether they were clear, relevant, and readable. |
| Features Extracted: |
| - 1. Text description of the bug |
| - 2. Number of lines of code affected by the bug |
| - 3. Timestamp of bug submission |
| - 4. Version control tags associated with the bug |
| - 5. Priority level assigned to the bug |
| - 6. Type of software component impacted by the bug |
| - 7. Operating system compatibility of the software |
| - 8. Programming language used to develop the software |
| - 9. Hardware specifications required to run the software |
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| Models Trained: |
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| Naive Bayes Classifier |
| Random Forest Classifier |
| Gradient Boosting Classifier |
| Neural Networks with Convolutional Layers |
| Hyperparameter tuning techniques: Cross-validation, Grid Search and Random Search applied to each model architecture. |
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| Metrics Used For Evaluation: |
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| Accuracy Score: Fraction of correctly predicted examples out of total examples. |
| Precision: Ratio of correct positive predictions over all positive predictions made by the model. |
| Recall: Ratio of true positives found among actual positives. |
| F1 score: Harmonic mean of precision and recall indicating |
