Machine Learning for Bug Classification: How It Actually Works

When you submit a bug report to an AI-powered triage system, what happens in those milliseconds before classification? This guide breaks down the ML pipeline that makes intelligent bug triage possible.

The Challenge

Bug reports are messy. They come in all forms:

"App crashed when I clicked the button"
"URGENT!!! Everything is broken!!!!"
"Hi, I was wondering if you could add dark mode? Thanks!"
"Getting error: TypeError: Cannot read property 'x' of undefined at line 42"

A classification system must handle all of these, distinguishing bugs from features, critical from cosmetic, and user errors from real issues.

The ML Pipeline

Step 1: Text Preprocessing

Raw text needs cleaning before ML models can process it effectively:

def preprocess(text: str) -> str:
    # Lowercase
    text = text.lower()
    # Remove excessive punctuation
    text = re.sub(r'!{2,}', '!', text)
    # Normalize whitespace
    text = ' '.join(text.split())    # Optional: remove code blocks for semantic analysis
    text = re.sub(r'

', '[CODE]', text, flags=re.DOTALL)
    return text
However, modern transformer models are robust enough that minimal preprocessing often works best—they've learned to handle messy text.
<h3>Step 2: Feature Extraction</h3>
Traditional ML approaches extract hand-crafted features:
<strong>Lexical Features:</strong>
Word count, character count
Presence of error keywords ("crash", "error", "broken")
Question marks (suggests question vs. bug)
Exclamation density (suggests urgency)
<strong>Semantic Features:</strong>
TF-IDF vectors
N-grams
Topic modeling scores
<strong>Structural Features:</strong>
Contains stack trace
Contains code blocks
Contains URLs/links
Format (prose vs. list)
<h3>Step 3: Embeddings (Modern Approach)</h3>Modern systems skip hand-crafted features entirely, using transformer models to generate dense vector representations:typescript
import OpenAI from 'openai';
const openai = new OpenAI();
async function getEmbedding(text: string): Promise {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: text,
  });
  return response.data[0].embedding; // 1536-dimensional vector
}
These embeddings capture semantic meaning: "app won't open" and "application fails to launch" produce similar vectors despite different words.
<h3>Step 4: Classification</h3>
#### Traditional ML ClassifiersWith features extracted, traditional classifiers like Random Forest or SVM can categorize:python
from sklearn.ensemble import RandomForestClassifier
Features: [word_count, has_error_keyword, has_question_mark, ...]
Labels: ['bug', 'feature_request', 'question', 'user_error']
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
Predict
prediction = clf.predict(new_report_features)
confidence = clf.predict_proba(new_report_features)
#### Neural ClassificationWith embeddings, a simple neural network classifier works well:python
import torch.nn as nn
class BugClassifier(nn.Module):
    def __init__(self, embedding_dim=1536, num_classes=4):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(embedding_dim, 256),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, num_classes)
        )
    def forward(self, x):
        return self.layers(x)
#### Zero-Shot Classification (LLMs)The simplest modern approach: ask an LLM directly.typescript
async function classifyBug(report: string): Promise {
  const response = await openai.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [
      {
        role: 'system',
        content:

Respond with JSON: {"classification": "...", "confidence": 0.0-1.0, "reasoning": "..."}}, { role: 'user', content: report } ], response_format: { type: 'json_object' }, });

return JSON.parse(response.choices[0].message.content); }

This approach requires no training data but has higher latency and cost per classification.
<h2>Confidence and Thresholds</h2>Classification without confidence is dangerous. Every prediction should include a score:

typescript
interface Classification {
  category: 'bug' 'feature' 'question'
 'user_error';
  confidence: number; // 0-1
  reasoning?: string;
}
function shouldAutoResolve(classification: Classification): boolean {
  // Only auto-resolve high-confidence user errors
  return (
    classification.category === 'user_error' &&
    classification.confidence >= 0.85
  );
}
function shouldAlert(classification: Classification): boolean {
  // Alert on any bug classified as critical with reasonable confidence
  return (
    classification.category === 'bug' &&
    classification.severity === 'critical' &&
    classification.confidence >= 0.70
  );
}
<h2>Multi-Label Classification</h2>
Real bug reports often fit multiple categories:
Bug that includes a feature request
Question that reveals a bug
User error caused by confusing UX
Multi-label classification handles this:python
Instead of mutually exclusive classes, predict probability for each
probabilities = model.predict_proba(embedding)
[0.8, 0.1, 0.3, 0.6] -> high bug, low feature, medium question, medium user_error
Apply thresholds per class
labels = [
    class_name
    for class_name, prob in zip(classes, probabilities)
    if prob > thresholds[class_name]
]
<h2>Severity Classification</h2>
Beyond category, severity matters. This typically requires a separate classifier:
<strong>Severity Levels:</strong>
<strong>Critical</strong>: Data loss, security vulnerability, complete breakage
<strong>High</strong>: Major feature broken, significant user impact
<strong>Medium</strong>: Feature partially broken, workaround available
<strong>Low</strong>: Minor issues, cosmetic bugs
<strong>Training Signals:</strong>
Keywords: "data loss", "security", "crash" → Critical
Emotion: High frustration indicators → Higher severity
Frequency: Multiple similar reports → Higher severity
User tier: Enterprise customer → Higher priority
<h2>The Training Process</h2>
<h3>Data Collection</h3>
You need labeled examples. Sources include:
Historical tickets with human classifications
Synthetic data generated by LLMs
Active learning (model uncertainty guides labeling)
<h3>Label Quality</h3>
Garbage labels → garbage model. Ensure:
Consistent labeling guidelines
Multiple reviewers for edge cases
Regular audits of label accuracy
<h3>Training</h3>python
Example: Fine-tuning for classification
from transformers import AutoModelForSequenceClassification, Trainer
model = AutoModelForSequenceClassification.from_pretrained(
    'microsoft/deberta-v3-small',
    num_labels=4
)
trainer = Trainer(
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()``

Evaluation

Key metrics:

Accuracy: Overall correct predictions
Precision: Of predicted bugs, how many are actually bugs?
Recall: Of actual bugs, how many did we catch?
F1: Harmonic mean of precision and recall

For bug classification, recall often matters more—missing a critical bug is worse than misclassifying a feature request.

BugBrain's Approach

BugBrain uses a hybrid approach:

Fast embedding-based classification for initial routing

LLM analysis for nuanced understanding and reasoning

Vector similarity search for documentation matching

Confidence-gated automation to balance speed and accuracy

This provides sub-second classification with high accuracy, while keeping costs manageable through tiered processing.

Building Your Own

If you're building bug classification:

Start Simple

Begin with keyword rules and upgrade to ML when rules break down.

Use Pre-trained Models

Don't train from scratch. Fine-tune embeddings or use zero-shot LLM classification.

Invest in Data Quality

Better labels beat better algorithms. Spend time on annotation guidelines.

Monitor Continuously

Track classification accuracy in production. Models drift as language evolves.

Keep Humans in the Loop

Low-confidence classifications should route to humans. Use their decisions as training signal.

Conclusion

Bug classification isn't magic—it's a well-understood ML pipeline combining text preprocessing, embeddings, and classification. Modern approaches using transformer embeddings and LLMs make it accessible without massive training datasets.

The hard part isn't the ML. It's building the surrounding system: feedback loops, confidence thresholds, escalation paths, and continuous improvement.

Want classification without building the pipeline? BugBrain handles the ML so you can focus on your product.