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Model Card: Bug Classification Algorithm
Purpose: To classify software bugs according to their clarity, relevance, and readability using a revamped dataset of historical bugs.
Model Type: Machine Learning Model (Supervised Learning)
Dataset Information:
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:
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- Text description of the bug
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- Number of lines of code affected by the bug
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- Timestamp of bug submission
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- Version control tags associated with the bug
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- Priority level assigned to the bug
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- Type of software component impacted by the bug
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- Operating system compatibility of the software
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- Programming language used to develop the software
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- Hardware specifications required to run the software
Models Trained:
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.
Metrics Used For Evaluation:
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
