| --- |
| language: en |
| tags: |
| - bug-classification |
| - severity-classification |
| - software-engineering |
| license: apache-2.0 |
| datasets: |
| - custom-github-issues |
| pipeline_tag: text-classification |
| --- |
| |
| # BugFlow Severity Classifier |
|
|
| Fine-tuned CodeBERT model for classifying bug report severity levels. |
|
|
| ## Labels |
| - **Low**: Minor issues, cosmetic changes |
| - **Medium**: Standard bugs affecting some functionality |
| - **High**: Important bugs affecting major functionality |
| - **Critical**: System crashes, data loss, security issues |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import RobertaTokenizer, RobertaForSequenceClassification |
| import torch |
| |
| model = RobertaForSequenceClassification.from_pretrained("YOUR_USERNAME/bugflow-severity-classifier") |
| tokenizer = RobertaTokenizer.from_pretrained("YOUR_USERNAME/bugflow-severity-classifier") |
| |
| text = "Application crashes when clicking login button" |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
| outputs = model(**inputs) |
| probs = torch.softmax(outputs.logits, dim=1) |
| labels = ['low', 'medium', 'high', 'critical'] |
| predicted = labels[torch.argmax(probs).item()] |
| print(f"Severity: {predicted}") |
| ``` |
|
|
| ## Training |
| - Base model: microsoft/codebert-base |
| - Dataset: Custom GitHub issues dataset + domain-specific bugs |
| - Fine-tuned using Hugging Face Transformers |
|
|
