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CloudBuild: Microsoft's distributed and caching build service

Authors:

Hamed Esfahani,

Alexei Kolomiets,

Wolfram Schulte,

Newton Sanches,

Srikanth KandulaAuthors Info & Claims

ICSE '16: Proceedings of the 38th International Conference on Software Engineering Companion

Pages 11 - 20

https://doi.org/10.1145/2889160.2889222

Published: 14 May 2016 Publication History

Abstract

Thousands of Microsoft engineers build and test hundreds of software products several times a day. It is essential that this continuous integration scales, guarantees short feedback cycles, and functions reliably with minimal human intervention. This paper describes CloudBuild, the build service infrastructure developed within Microsoft over the last few years. CloudBuild is responsible for all aspects of a continuous integration workflow, including builds, test and code analysis, as well as drops, package and symbol creation and storage. CloudBuild supports multiple build languages as long as they fulfill a coarse grained, file IO based contract. CloudBuild uses content based caching to run build-related tasks only when needed. Lastly, it builds on many machines in parallel. CloudBuild offers a reliable build service in the presence of unreliable components. It aims to rapidly onboard teams and hence has to support non-deterministic build tools and specification languages that under-declare dependencies. We will outline how we addressed these challenges and characterize the operations of CloudBuild. CloudBuild has on-boarded hundreds of codebases with only man-months of effort each. Some of these codebases are used by thousands of developers. The speed ups of build and test range from 1.3× to 10×, and service availability is 99%.

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Cited By

Kamath DAdams BHassan A(2025)Lightweight dynamic build batching algorithms for continuous integrationEmpirical Software Engineering10.1007/s10664-024-10555-430:2Online publication date: 8-Jan-2025
https://doi.org/10.1007/s10664-024-10555-4
Wang JWang KNie PFilkov VRay BZhou M(2024)Efficient Incremental Code Coverage Analysis for Regression Test SuitesProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695551(1882-1894)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695551
Zhang GLiu LChen ZWang JFilkov VRay BZhou M(2024)Hybrid Regression Test Selection by Integrating File and Method DependencesProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695525(1557-1569)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695525
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Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '16: Proceedings of the 38th International Conference on Software Engineering Companion

May 2016

946 pages

ISBN:9781450342056

DOI:10.1145/2889160

General Chair:
Laura Dillon
Michigan State University
,
Program Chairs:
Willem Visser
Stellenbosch University, South Africa
,
Laurie Williams
North Carolina State University

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\TCSE: TC on Software Engineering
IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2016

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ICSE '16

Sponsor:

ACM
SIGSOFT
IEEE-CS\TCSE
IEEE-CS\DATC

ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Kamath DAdams BHassan A(2025)Lightweight dynamic build batching algorithms for continuous integrationEmpirical Software Engineering10.1007/s10664-024-10555-430:2Online publication date: 8-Jan-2025
https://doi.org/10.1007/s10664-024-10555-4
Wang JWang KNie PFilkov VRay BZhou M(2024)Efficient Incremental Code Coverage Analysis for Regression Test SuitesProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695551(1882-1894)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695551
Zhang GLiu LChen ZWang JFilkov VRay BZhou M(2024)Hybrid Regression Test Selection by Integrating File and Method DependencesProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695525(1557-1569)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695525
McIntosh SSpinellis DConstantinou EBacchelli A(2024)Mining Our Way Back to Incremental Builds for DevOps PipelinesProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3649106(48-49)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3649106
Alfadel MMcIntosh SRoychoudhury APaiva AAbreu RStorey M(2024)The Classics Never Go Out of Style: An Empirical Study of Downgrades from the Bazel Build TechnologyProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639169(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639169
An GYoon JBach THong JYoo S(2024)Just-in-Time Flaky Test Detection via Abstracted Failure Symptom Matching2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58944.2024.00078(741-752)Online publication date: 6-Oct-2024
https://doi.org/10.1109/ICSME58944.2024.00078
Priyanka Valmiki Dr. Arvind Kaurav Prof. Nehul Mathur (2023)A Literature Survey on Privacy Preserving in Image processing on CloudInternational Journal of Scientific Research in Science and Technology10.32628/IJSRST52310562(460-469)Online publication date: 1-Oct-2023
https://doi.org/10.32628/IJSRST52310562
Shen HPszeniczny KLavaee RKumar STallam SLi XAamodt TJerger NSwift M(2023)Propeller: A Profile Guided, Relinking Optimizer for Warehouse-Scale ApplicationsProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575727(617-631)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3575693.3575727
Augusto C(2023)Toward an efficient End-to-End test suite execution2023 IEEE 34th International Symposium on Software Reliability Engineering Workshops (ISSREW)10.1109/ISSREW60843.2023.00038(26-29)Online publication date: 9-Oct-2023
https://doi.org/10.1109/ISSREW60843.2023.00038
He JMacQueen RBombardieri NAli KWright JCifuentes C(2023)Finding an Optimal Set of Static Analyzers To Detect Software Vulnerabilities2023 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58846.2023.00060(463-473)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICSME58846.2023.00060
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