2026年开发者调查报告_57页_869kb

报告摘要

State of Code Developer Survey Report Summary

Core Content

This report, based on a survey of 1,149 professional developers, explores the current state of AI in software development, focusing on usage patterns, effectiveness, and the emerging challenges and opportunities.

Main Findings

AI Adoption and Usage

AI is now a daily tool: 72% of developers who have tried AI coding tools use them every day.
AI-generated code is widespread: 42% of developers' code is currently AI-assisted or generated, with predictions that this will rise to over 50% by 2027.
AI is used across all project types: From prototypes (88%) to mission-critical services (58%), developers are integrating AI into various stages of development.
Top AI tools: GitHub Copilot (75%) and ChatGPT (74%) are the most widely used AI coding tools, followed by Claude (48%), Gemini/Duet AI (37%), and Cursor (31%).

Effectiveness vs. Adoption

A gap exists between AI usage and effectiveness: While AI is used frequently, developers report that it is only effectively used for specific tasks like writing documentation (74% effective), explaining code (66% effective), and generating tests (59% effective).
Code review and debugging are less effective: Only 47% and 44% of developers find AI effective for these tasks, respectively.

Developer Trust in AI

High skepticism: 96% of developers do not fully trust that AI-generated code is functionally correct.
Verification is critical: Only 48% of developers always check their AI-assisted code before committing, indicating a verification bottleneck.
Effort required for quality: 61% of developers agree that getting good code from AI requires significant effort in prompting and fixing.

Skills in the AI Era

Reviewing and validating AI code is the most important new skill for developers (47%).
Efficient prompting is also highly valued (42%).
Other important skills include translating domain knowledge into code requirements (27%), architecting systems (25%), and identifying security risks (24%).

Tool Access and Adoption

Shadow adoption is common: Over 50% of developers use ChatGPT through personal accounts, and 63% use Perplexity that way.
Formal adoption varies: GitHub Copilot and Amazon Q Developer are more likely to be used through work-sanctioned accounts (78% and 72% respectively).
Tool choice differs by company size: SMBs prefer ChatGPT, Claude, and JetBrains, while mid-sized and enterprise developers lean toward infrastructure and deployment automation.

AI Agents and Their Role

Agentic AI is gaining traction

64% of developers have started using AI agents, with 25% using them regularly.
AI agents are most effective for:
- Creating code documentation (68%)
- Automated test generation and execution (61%)
- Automated code review (57%)

Less common use cases

Security vulnerability patching (28%)
Automated debugging (44%)
Deployment pipeline management (33%)

The New Developer Toil

AI reduces toil, but does not eliminate it

75% of developers believe AI reduces the amount of time spent on toil work.
However, developers still spend 23-25% of their workweek on toil tasks, such as managing technical debt and debugging legacy code.
Toil is shifting: Less frequent AI users face traditional toil (e.g., understanding code), while frequent users encounter new toil (e.g., managing technical debt).

AI’s Impact on Key Metrics

Impact Area	Positive Impact (%)
Developer productivity	89%
Time-to-market	70%
Feature/fix release frequency	60%
Code quality	58%
Code maintainability	56%
End-user experience	47%
Technical debt	47%
Rework / patch costs	42%
Defect rates	39%
Vulnerability rates	34%
Outage frequency	25%
Outage severity	24%

Key Takeaways

AI is a daily part of the development workflow, but effectiveness is uneven.
Verification is a major bottleneck, with developers spending significant time reviewing and fixing AI-generated code.
Trust remains low, especially for code that is critical or mission-focused.
AI agents are beginning to automate key tasks like documentation and testing, but not yet complex maintenance or security tasks.
Tool sprawl is growing, with developers using multiple AI tools daily, often through personal accounts, creating risks in terms of security and compliance.
The shift in toil suggests that AI is not reducing overall workload but changing its nature, requiring new skills and approaches in code review and maintenance.

Recommendations for Engineering Leaders

Implement systematic verification processes to ensure code quality and security.
Address the "bring your own AI" (BYOAI) culture by promoting governance and secure tool access.
Focus on training developers in reviewing and validating AI-generated code.
Monitor and support tool adoption trends, especially for smaller organizations and junior developers.

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