metadata
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
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