2026软件工程基准报告_AI生产力版_48页_1mb

报告摘要

2026 Software Engineering Benchmarks Report Summary

Introduction

The 2026 Software Engineering Benchmarks Report is the fifth annual edition, analyzing over 8.1 million pull requests from 4,800 development teams in 42 countries. The report explores how AI is impacting software development, beyond traditional metrics. It introduces a new qualitative dimension to complement quantitative data, offering insights from the 2026 AI in Engineering Leadership Survey. The goal is to help engineering leaders understand not only where their teams stand but also why, providing data and perspective for smarter decisions in the AI era.

Core Content and Key Metrics

The report categorizes teams into four performance levels: Elite (top 10%), Good (top 30%), Fair (top 60%), and Needs focus (bottom 40%). All data is anonymized and normalized, using P75 (75th percentile) for aggregation to reduce sensitivity to outliers.

Delivery Metrics

Metric	Elite	Good	Fair	Needs focus
Coding Time (hours)	< 54 mins	54 mins - 4h	5 - 23h	> 23h
Pickup Time (hours)	< 1h	1 - 4h	5 - 16h	> 16h
Approve Time (hours)	< 10h	10 - 22h	23 - 42h	> 42h
Merge Time (hours)	< 1h	1 - 3h	4 - 16h	> 16h
Review Time (hours)	< 3h	3 - 14h	15 - 24h	> 24h
Deploy Time (hours)	< 16h	16 - 106h	107 - 277h	> 277h
Cycle Time (hours)	< 25h	25 - 72h	73 - 161h	> 161h
Merge Frequency	> 2.0	2 - 1.2	1.2 - 0.66	< 0.66
Deploy Frequency	> 1.2	1.2 - 0.5	0.5 - 0.2	< 0.2
PR Size (code changes)	< 100	100 - 155	156 - 228	> 228
PR Maturity (%)	> 89%	89 - 83%	82 - 77%	< 77%

Predictability Metrics

Metric	Elite	Good	Fair	Needs focus
Change Failure Rate	< 1%	1 - 4%	5 - 17%	> 17%
Refactor Rate (%)	< 11%	11 - 16%	17 - 22%	> 22%
Rework Rate (%)	< 3%	3 - 5%	6 - 8%	> 8%
Capacity Accuracy (%)	85 - 115%	75 - 85% or 115 - 125%	70 - 75% or 125 - 130%	> 70% or > 130%
Planning Accuracy (%)	> 82%	82% - 64%	63% - 47%	< 47%

Project Management Metrics

Metric	Elite	Good	Fair	Needs focus
Issues Linked to Parents (%)	> 90%	90 - 67%	66 - 56%	< 56%
Branches Linked to Issues (%)	> 77%	77 - 62%	61 - 41%	< 41%
In Progress Issues with Estimation (%)	> 55%	55 - 26%	25 - 14%	< 14%
In Progress Issues with Assignees (%)	> 96%	96 - 84%	83 - 76%	< 76%

New This Year: Acceptance Rate Benchmarks

PR Type	Elite	Good	Fair	Needs focus
All PRs	> 95%	91 - 95%	85 - 90%	< 85%
Manual PRs	> 95%	92 - 95%	87 - 91%	< 87%
Agentic AI PRs	> 71%	61 - 71%	42 - 60%	< 42%

AI PRs have a significantly lower Acceptance Rate than manual PRs.
The Acceptance Rate for AI PRs is less than half that of manual PRs (32.7% vs. 84.4%).
This highlights the challenges in merging AI-generated code, such as unclear ownership and higher complexity.

AI Productivity Insights

AI in the SDLC

Agentic AI PRs are 2.6x larger than Unassisted PRs.
AI-Assisted PRs have a 5.3x longer PR Pickup Time than Unassisted PRs.
The Acceptance Rate for AI PRs is less than half that of manual PRs, showing the current limitations in integrating AI into workflows.

Qualitative Insights

Engineering leaders identify lack of context as a major barrier to merging AI-generated PRs.
AI often generates larger and more complex changes, which reviewers find harder to understand and trust.
AI-assisted PRs are reviewed faster than Unassisted PRs, but Agentic AI PRs take the longest to review, indicating a lack of clear ownership and increased complexity.
Trust in AI-generated code remains a significant concern, with many leaders unsure about its reliability and quality.

Conclusion: What's Next for AI?

The report underscores the need for engineering leaders to understand both the quantitative and qualitative impacts of AI on software development. While AI is increasing code output, it is also creating new challenges in review, testing, and governance. The integration of AI into workflows is still in its early stages, and teams are struggling with how to effectively manage and trust AI contributions. The report aims to guide leaders in navigating these challenges and optimizing AI usage for better productivity and code quality.

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