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--- |
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language: |
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- en |
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tags: |
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- text-classification |
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widget: |
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- text: The app crashed when I opened it this morning. Can you fix this please? |
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example_title: Likely bug report |
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- text: Please add a like button! |
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example_title: Unlikely bug report |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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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). |
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## Model Details |
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### Model Description |
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- **Developed by:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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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 |
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Split into training and validation sets - Training Data consists of approximately 80% of data and validation/testing data comprises of the remaining 20%. |
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Each example contains features including descriptions of software bugs along with human annotations specifying whether they were clear, relevant, and readable. |
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Features Extracted: |
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- 1. Text description of the bug |
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- 2. Number of lines of code affected by the bug |
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- 3. Timestamp of bug submission |
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- 4. Version control tags associated with the bug |
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- 5. Priority level assigned to the bug |
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- 6. Type of software component impacted by the bug |
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- 7. Operating system compatibility of the software |
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- 8. Programming language used to develop the software |
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- 9. Hardware specifications required to run the software |
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Models Trained: |
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Naive Bayes Classifier |
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Random Forest Classifier |
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Gradient Boosting Classifier |
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Neural Networks with Convolutional Layers |
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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. |
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Precision: Ratio of correct positive predictions over all positive predictions made by the model. |
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Recall: Ratio of true positives found among actual positives. |
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F1 score: Harmonic mean of precision and recall indicating |
