Framework

DORA

Four key metrics for measuring software delivery performance: deployment frequency, lead time, MTTR, and change fail rate

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Framework

DORA

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Framework

DORA

Description

Four key metrics for measuring software delivery performance: deployment frequency, lead time, MTTR, and change fail rate

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3 Mentioned In
33 Related Concepts
16 Related Practices

Transcript Mentions

DORA 4, there are four key metrics. There's two speed metrics, deployment frequency and lead time. So code commit to code deploy. There's stability metrics, MTTR and change fail rate. If those are used to assess the speed of the pipeline and the general performance of the pipeline, that's great. If you're trying to use those to understand... Because implied in that is feedback loops, right, because you used to kind of get feedback from customers. But we can't just use that blindly now when we're using AI, as an example, because we have feedback loops much earlier and not even just at the local build and test phase. We have feedback loops throughout, and even sometimes in the middle of some of the pipeline, that we really want to leverage in ways that weren't as useful before. I won't say they weren't possible, but we just didn't really focus there.

...ure engineers well. So to me, it feels like developer happiness is the ultimate metric. We had Nicole on the podcast who came up with this framework, DORA, that's an interesting way of just measuring developer experience, developer happiness.

Nicole ForsgrenSource summary

Source summary: Measures delivery speed and reliability.

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  • How to measure AI developer productivity in 2025 | Nicole Forsgren · mentions framework · DORA
  • The future of AI in software development | Inbal Shani (CPO of GitHub) · mentions framework · DORA
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · mentions framework · DORA
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · features · Nicole Forsgren
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · features · Nicole Forsgren
  • The future of AI in software development | Inbal Shani (CPO of GitHub) · features · Inbal Shani
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · discusses · Feedback Loops
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · discusses · Time to Value
  • The future of AI in software development | Inbal Shani (CPO of GitHub) · discusses · Time to Value
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · discusses · Cognitive Load
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · discusses · Cognitive Load
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · shares practice · Listening Tour
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · shares practice · Make work accessible to key audiences
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · shares practice · Make work accessible to key audiences
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · shares practice · Start with problem definition before metrics

Source mentions

Podcast excerpts and source summaries from the explorer. Entries marked “Source summary” are summaries, not transcript quotations.

How to measure AI developer productivity in 2025 | Nicole Forsgren

Nicole Forsgren · 00:15:01

DORA 4, there are four key metrics. There's two speed metrics, deployment frequency and lead time. So code commit to code deploy. There's stability metrics, MTTR and change fail rate. If those are used to assess the speed of the pipeline and the general performance of the pipeline, that's great. If you're trying to use those to understand... Because implied in that is feedback loops, right, because you used to kind of get feedback from customers. But we can't just use that blindly now when we're using AI, as an example, because we have feedback loops much earlier and not even just at the local build and test phase. We have feedback loops throughout, and even sometimes in the middle of some of the pipeline, that we really want to leverage in ways that weren't as useful before. I won't say they weren't possible, but we just didn't really focus there.