Accelerate: The Science of Lean Software and DevOps

by Nicole Forsgren, Jez Humble, Gene Kim

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Forsgren, Humble, and Kim present four years of State of DevOps survey research identifying the specific technical and cultural capabilities that predict high software delivery performance. Their headline metrics, deployment frequency, lead time, mean time to restore, and change-fail rate, have become the industry standard (DORA metrics) for measuring engineering organisation performance.

Published:: 2018
Pages:: 288

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In the Conversation

In this collection, Accelerate: The Science of Lean Software and DevOps references 5 other books.

It draws on Continuous Delivery, The Phoenix Project and The DevOps Handbook.

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What This Book Draws On

The books Kim references and why each one mattered to the argument.

Accelerate identifies continuous delivery practices (trunk-based development, deployment automation, test automation) directly from Humble and Farley's book as the strongest statistical predictors of delivery performance.

References

Continuous Delivery

by Jez Humble

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Forsgren, Humble, and Kim reference Kim's own Phoenix Project as the narrative foundation for the DevOps practices they empirically validate in this book.

References

The Phoenix Project

by Gene Kim

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Accelerate operationalizes and statistically validates the practices catalogued in The DevOps Handbook, co-authored by the same Humble and Kim.

References

The DevOps Handbook

by Gene Kim

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The book frames its lean-software argument using Ries's build-measure-learn loop from The Lean Startup, treating short deployment cycles as the technical enabler of lean experimentation.

References

The Lean Startup

by Eric Ries

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Accelerate builds on Humble's own Lean Enterprise, extending its case studies into statistically rigorous survey evidence on high-performing technology organisations.

References

The Lean Enterprise

by Jez Humble

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Directly cites

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The Pragmatic Programmer

The Mythical Man-Month

Refactoring

Clean Code

Good to Great

The Innovator's Dilemma

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