AI-Assisted Software Development Survey 2026

Thank you for participating in this survey.

The goal of this survey is to better understand how developers currently use AI-assisted software development tools, the challenges they face, and how these tools could evolve in the future.

The survey is completely anonymous and should take approximately 5–8 minutes to complete.

SECTION 1 — About You

How many years of professional software development experience do you have?

Student / Learning

More than 10 years

Which best describes your current role?

Full Stack Engineer

DevOps / Platform Engineer

AI / Machine Learning Engineer

Engineering Manager / Tech Lead

What is the size of your organization?

Solo Developer / Freelancer

Startup (1–20 employees)

Small Business (21–100)

Medium Organization (101–1000)

Enterprise (1000+)

Which AI coding tools do you actively use?

JetBrains AI Assistant

SECTION 2 — Your AI Development Workflow

How often do you use AI while developing software?

Multiple times every day

What do you primarily use AI for?

Refactoring existing code

Understanding unfamiliar code

Architecture discussions

Writing documentation

DevOps / Infrastructure

Learning new technologies

Approximately what percentage of your development work involves AI assistance?

SECTION 3 — Current Challenges

Which parts of AI-assisted development consume the most of your attention?

Explaining requirements

Clarifying requirements

Reviewing generated code

Debugging AI-generated code

Switching between repositories

Integrating frontend and backend

Waiting for AI responses

Correcting architecture decisions

How often do you need to re-prompt or rephrase your requests before getting a satisfactory result?

What are the most common reasons you re-prompt AI?

Misunderstood the requirement

Lacked repository context

Didn't follow project conventions

Modified unrelated code

Generated incorrect business logic

Didn't ask enough questions

Asked too many unnecessary questions

Hallucinated APIs or functions

Which of these do you usually verify manually after AI generates code?

Authentication / Authorization

Approximately how much of your development time is spent supervising, correcting, or verifying AI-generated work?

What is currently your biggest frustration when using AI for software development?

SECTION 4 — Trust & Control

On average, how much do you trust AI-generated code before reviewing it?

Which changes would you always want to review before AI continues?

Database schema changes

API contract changes

Authentication / Authorization

Infrastructure changes

Security-sensitive code

Deployment configuration

Complete the following sentence: "I lose confidence in AI-generated code when..."

SECTION 5 — Looking Forward

Which improvements would have the biggest impact on your productivity?

Better understanding of my repository

Better planning before implementation

Better adherence to project conventions

Better documentation

Better integration across multiple repositories

More reliable code generation

If your preferred AI coding tool significantly improved in the areas most important to you, how likely would you be to increase your reliance on it?

Looking ahead 2–3 years, what role do you think AI will realistically play in your software development workflow?

Mostly a code autocomplete tool

A coding assistant for specific tasks

A collaborator for implementing features

Responsible for implementing complete features with my supervision

Responsible for most implementation while I focus on architecture and review

During a typical feature implementation, where do you spend the most mental effort?

Understanding the existing codebase

Defining requirements

Designing the solution

Writing backend code

Writing frontend code