🔍 Why Predict Assignees?

• 🔄 Multiple handoffs
• ⏳ Increased time to resolution
• 😓 Frustration among devs and QA teams

By applying machine learning to historical bug data, we can predict the most likely engineer for a new ticket with 80–90% accuracy (based on Bugflows' internal benchmarks). That’s hours of saved effort every week.

📁 Step 1: Export Your Jira Data

First, get your Jira issues exported as CSV. You'll need at least these columns:

• summary
• description
• assignee
• created

👉 Jira CSV Export Docs

🛠️ Step 2: Install Python Libraries

We'll use some core data science tools:

pip install pandas scikit-learn nltk matplotlib

🧹 Step 3: Preprocess Your Data

Clean and prepare the data for modeling.

import pandas as pd
import nltk
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

nltk.download('stopwords')
from nltk.corpus import stopwords

df = pd.read_csv('jira_issues.csv')
df['text'] = df['summary'].fillna('') + ' ' + df['description'].fillna('')
df = df[df['assignee'].notnull()]
top_assignees = df['assignee'].value_counts().nlargest(10).index
df = df[df['assignee'].isin(top_assignees)]
X_train, X_test, y_train, y_test = train_test_split(df['text'], df['assignee'], test_size=0.2, random_state=42)
tfidf = TfidfVectorizer(stop_words=stopwords.words('english'), max_features=5000)
X_train_vec = tfidf.fit_transform(X_train)
X_test_vec = tfidf.transform(X_test)
                                                    

🤖 Step 4: Train the Model

We’ll use a simple but effective Logistic Regression classifier.

 model = LogisticRegression(max_iter=1000)
model.fit(X_train_vec, y_train)
y_pred = model.predict(X_test_vec)
print(classification_report(y_test, y_pred))
                                                    

Sample output:

    precision    recall  f1-score   support

  alice      0.89       0.82      0.85        45
  bob        0.76       0.88      0.81        51
  ...
accuracy                           0.84       400

                                                    

📊 Step 5: Analyze & Improve

• Visualize confusion matrix to spot misclassifications
• Try advanced models like RandomForestClassifier or XGBoost
• Use Issue Type, Component, or Labels as additional features
• Train weekly to reflect team changes

🔄 Bonus: Deploy as a Microservice

You can use FastAPI to serve this model via an endpoint:

from fastapi import FastAPI
from pydantic import BaseModel
import joblib

class Bug(BaseModel):
    summary: str
    description: str

app = FastAPI()
model = joblib.load("assignee_model.pkl")
vectorizer = joblib.load("vectorizer.pkl")

@app.post("/predict")
def predict(bug: Bug):
    text = bug.summary + " " + bug.description
    vec = vectorizer.transform([text])
    pred = model.predict(vec)
    return {"predicted_assignee": pred[0]}

🧩 Real-World Examples

• Microsoft uses ML to triage issues in large codebases
• Facebook uses predictive tools for Messenger bugs
• Bugflows achieves 86%+ accuracy in enterprise setups

🔍 Key Takeaways

• scikit-learn + TF-IDF is a powerful baseline
• Automation = less toil, faster releases, happier engineers
• Training weekly ensures models adapt to team changes

⚙️ Want This Integrated in Your Org?

Bugflows builds end-to-end ML solutions for bug data. Book a demo and get started in days.