Synthetic Data

Exploring the Synthetic AI Developer Productivity Dataset

Team Syncora

August 22, 2025

Understanding AI developer productivity metrics is important for organizations that want to optimize workflows, improve team performance, and prevent burnout.

As AI is being used more in developer analytics and team management, it’s more important than ever to work with datasets that capture focus hours, task completion, and burnout signals. But the old-age question still remains:

Where do you get real-world developer productivity data when it raises privacy concerns and ethical issues around employee monitoring?

The answer is synthetic data: it is privacy-safe, realistic, and free from compliance risks. You can generate synthetic data with tools like Syncora.ai or download a synthetic AI developer productivity dataset from GitHub below.

What is the Synthetic AI Developer Productivity Dataset About?

The dataset simulates realistic developer behaviors around

Focus hours

Coding output

Meetings

Reported burnout

It has zero risk of exposing individual identities (zero PII leaks). This makes it a privacy-safe developer analytics data source and is suitable for a wide variety of purposes, such as machine learning and behavioral research.

Each record has daily work habits and productivity markers. This will help teams and researchers understand how developers allocate their time, how burnout signs manifest, and how overall efficiency trends evolve under different workloads.

Get Synthetic Developer Productivity Dataset

The privacy-safe developer analytics data is a carefully generated collection of 5,000 high-fidelity synthetic records created with Syncora.ai’s advanced synthetic data engine.

Key Behavioral Features Included

This synthetic developer productivity data has a comprehensive set of variables relevant to developer workflows and well-being, such as:

focus_hours: Daily hours spent in uninterrupted deep work (0–8)

meetings_per_day: Number of meetings attended each day (0–6)

lines_of_code: Average lines of code written per day (0–1000)

commits_per_day: Number of git commits per day (0–20)

task_completion_rate: Percentage of assigned tasks completed daily (0–100%)

reported_burnout: Self-reported burnout indicator (0 for low, 1 for high)

debugging_time: Hours spent on debugging (0–5)

tech_stack_complexity: Complexity score of the tech stack used (1–10)

pair_programming: Whether pair programming occurred (0 for no, 1 for yes)

productivity_score: Composite score summarizing overall developer output (0–100)

Dataset Characteristics and Format

Size: 5,000 synthetic records simulating daily developer productivity across various dimensions.

Format: Ready-to-use CSV files compatible with Python, R, Excel, and other data analysis tools.

Data Privacy: Fully synthetic with no real user data, offering zero privacy liability.

Utility: Preserves realistic relationships among variables while supporting complex modeling and analytics tasks.

Applications of This Dataset in AI and Workflow Analytics

The synthetic AI developer productivity dataset has diverse research and practical use cases:

Productivity Prediction: You can train machine learning models that forecast developer output based on task load and behavioral cues.

Burnout Detection: Build early warning classifiers for detecting developers at risk of burnout from work patterns.

Feature Engineering Practice: Improve skills in handling mixed data types and missing values through real-world-like task data.

Analytics Dashboards: Create functional productivity visualization tools for team leads and engineering managers.

AI Team Simulation: Model and test HR, time tracking, and project planning tools in simulated yet realistic environments.

In short, this dataset offers a risk-free playground for innovation in developer workflow management and well-being analytics.

How to Generate Synthetic Developer Productivity Data in 2025?

There are two approaches to generating synthetic productivity datasets:

A) Manual Method:

Start with anonymizing real-world productivity data. Next, define the key productivity and behavioral features to be included in the dataset. Carefully structure the schema, paying attention to variable types and their relationships. To generate the data, apply methods such as rule-based synthesis, statistical sampling, or generative AI models (e.g., GANs or VAEs). Follow certain processes and generate synthetic data while tuning/testing it. Finally, validate the synthetic dataset to ensure it reflects accuracy, balance, and realism.

B) Using Synthetic Data Generation Platform

An alternative and more efficient approach is to use platforms such as Syncora.ai. Start by uploading raw or schematic developer productivity data. The platform’s AI agents automatically clean, structure, and synthesize high-quality synthetic datasets within minutes. Researchers and practitioners can then download ready-to-use, privacy-compliant data to accelerate both model training and analysis.

FAQs

1) Is this dataset really privacy-safe, and can I share results publicly?

Yes. A synthetic dataset does not contain PII or real-user records, so you can analyze, publish charts, and share insights openly.

2) Can I build accurate models with a synthetic developer productivity data source?

You can build strong baseline models if the synthetic developer productivity data preserves realistic distributions and correlations (e.g., focus hours vs. task completion rate, meetings vs. productivity score). You should validate on any available real data later to fine-tune thresholds and improve generalization.

To Sum it Up

The synthetic AI developer productivity dataset offers a privacy-safe, high-realism resource for analyzing AI developer behaviors and workflow dynamics. It lets researchers, team leads, and AI developers build analytic solutions to enhance productivity, detect burnout early, and optimize team performance without legal or ethical concerns. With tools like Syncora.ai, you can generate or access such datasets quickly, or you can download a readily available privacy-safe developer analytics dataset.