2024 Software Engineering Benchmarks Report
This report was created from a study of 2,000+ dev teams across 64 countries and more than 3,600,000 pull requests.
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P75
Averages
*Calculated using 75th percentile
Toggle to see Averages BenchmarksElite
Good
Fair
Needs Focus
Coding Time
< 0.5< 19hours
0.5 - 2.519 - 44hours
2.5 - 2444 - 99hours
> 24> 99hours
Pickup Time
< 1< 7hours
1 - 37 - 13hours
3 - 14 13 - 24hours
> 14> 24hours
Review Time
< 0.5< 5hours
0.5 - 35 - 14hours
3 - 18
14 - 29
hours> 18> 29hours
Merge Frequency
> 2> 2per dev/week
2 - 1.52 - 1.5per dev/week
1.5 - 11.5 - 1
per dev/week
< 1< 1per dev/week
Deploy Time
< 3< 6hours
3 - 696 - 50hours
69 - 197
50 - 137hours> 197> 137hours
Cycle Time
< 19< 73hours
19 - 6673 - 155hours
66 - 218
155 - 304hours> 218> 304hours
Deployment Frequency
> 1/day> 1/dayper service
> 2/week> 2/weekper service
1-2/week
1-2/weekper service< 1/week< 1/weekper service
Change Failure Rate
< 1%< 1%
1% - 8%1% - 8%
8% - 39%
8% - 39%> 39%
> 39%
MTTR
< 7< 7hours
7 - 97 - 9hours
9 - 10
9 - 10hours> 10> 10hours
PR Size
< 98
< 219
code changes98 - 148219 - 395code changes
148 - 218
395 - 793code changes> 218> 793code changes
Rework Rate
< 2%< 2%
2% - 5%2% - 5%
5% - 7%
5% - 7%> 7%> 7%
Refactor Rate
< 9%< 9%
9% - 15%9% - 15%
15% - 21%
15% - 21%> 21%> 21%
Planning Accuracy
> 85%
> 85%
85% - 60%85% - 60%
60% - 40%
60% - 40%< 40%< 40%
Capacity Accuracy
85% - 115%85% - 115%
Ideal Range
above 130%
above 130%
Under Commit
116% - 130%
116% - 130%Potential Under Commit
70% - 84%70% - 84%
Potential Over Commit
Data Sourced From
2,000+
Teams
3.6M
PRs
64
Countries
“We're excited to partner with LinearB on this year's Accelerate State of DevOps Report, which helps shape the future of developer productivity. LinearB's benchmarks research adds essential insights into how engineering teams continue to evolve through quantitative support to DORA's research.”
Nathen Harvey
Head of Google's DORA Team
|
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DORA, Efficiency & Planning Benchmarks
The best way to improve any team is to establish a baseline for what “good” means. The 2024 Engineering Benchmarks Report is the most thorough, comprehensive look at what performance metrics make engineering orgs elite, average, or needing improvement.
See benchmarks for essential metrics like:
Cycle time, deployment frequency, mean time to restore, and more
Pull request size, code review time, pickup time, and more
Planning accuracy, capacity accuracy, investment distribution, and more
Benchmarks Based On Org Size & Maturity
Small teams need to benchmark their performance against an org with 5 engineers, not 5,000. In this year’s report, we outline the benchmarks and insights that apply to engineering teams based on their org size.
See benchmarks for companies broken down by the following sizes:
Startups 0-100 employees
Scale-ups 100-1,000 employees
Enterprise 1,000+ employees
See Where You Stack Up
Plan and execute project goals confidently with LinearB
For the First Time: Investment Benchmarks
With investment metrics, leaders can see what percentage of their team’s time and resources are going toward different initiatives. With this visibility, engineering leaders can make strategic decisions around what work should be prioritized moving forward.
See investment benchmarks broken down by the following resource allocations:
New value
Feature enhancements
Developer experience
Keeping the lights on
Engineering Benchmarks Report Release Webinar
In this webinar, LinearB CTO Yishai Beeri and Product Manager Gal Rubin present fascinating new insights into how elite engineering teams work, set goals and achieve success.
Over the course of 30 minutes, Yishai and Gal cover:
The all-new Engineering Investment Benchmarks
Data insights by organization / team / size / geo / industry
How elite teams are performing against the four DORA metrics
Watch Here