BugFlows

From Chaos to Clarity: Automating Assignee Prediction with AI/ML (86% Accuracy!)

Managing software bugs in fast-moving development teams is messy. Triage meetings take time. Prioritization often feels like guesswork. And assigning issues to the right engineer? That’s one of the most subjective — and time-consuming — parts of the process.
At Bugflows, we decided to fix that.
We built a machine learning model that predicts the right assignee for incoming defect reports — and it works. In recent tests, our model achieved 86% accuracy on unseen validation data. This post breaks down how we got there, and why it matters.

🔍 The Problem: Manual Bug Assignment Is Broken

When a defect is reported, someone — usually a tech lead or project manager — has to decide who should fix it. This decision is based on a mix of:

Who knows this area of the code?
Who has worked on this feature before?
Who’s available?
Gut feeling.

This process doesn’t scale. It introduces delays, errors, and uneven workloads. Worse, it diverts engineering effort from what really matters: fixing the problem.

🧠 The Vision: Predict Assignees Automatically

Our idea was simple: what if we could train an AI/ML model to learn from past bug reports and automatically suggest the right assignee?
The inputs: historical bug tickets — including title, description, component, and prior assignments.
The output: the most likely engineer who should handle the issue.

🔬 The Approach: ML Pipeline at a Glance

We trained our model using hundreds of real-world defect reports from production environments. Here’s how we did it:

Data Preparation: Extracted bug titles, descriptions, components, priorities, and assignees.
Feature Engineering: Used NLP techniques (TF-IDF, embeddings) to convert unstructured text into model-friendly formats.
Model Selection: Tested multiple models (Random Forest, XGBoost, Logistic Regression) and ensemble strategies.
Training & Validation: Used stratified k-fold cross-validation with holdout sets to evaluate performance on unseen data.
Evaluation Metric: Focused on top-1 accuracy — does the model's top suggestion match the actual assignee?

📊 The Results: 86% Accuracy on Unseen Data

On the final validation set, our best-performing model achieved:

✅ 86.99% accuracy for assignee prediction
📉 Significant reduction in assignment delays
⚡ Faster triage for high-priority issues

For context, random assignment in this environment has <5% accuracy. So this is not just an improvement — it’s a transformation.

💡 Why This Matters

Scalability: Teams can triage hundreds of bugs with minimal human involvement.

Fairness: Workload is distributed based on historical expertise, not biases.
Speed: Time-to-fix improves dramatically.
Learning: The model gets better with every new issue it sees.

🚀 What’s Next

We’re actively integrating this model into the Bugflows platform, so teams can benefit from AI/ML-driven triage right inside their workflows. Our vision is a fully intelligent defect lifecycle — from prediction to resolution. If you're interested in seeing it in action or want to try this for your team, get in touch.

🧠 Final Thought

Software engineering shouldn't be slowed down by manual, repetitive decisions. With the right data and smart AI/ML, we can eliminate chaos — and bring clarity to the bug management process.
Let’s automate what slows us down, so we can focus on what moves us forward.