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John Ewart

dblp:322/4619 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software maintenance and evolution · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution › release engineering
continuous integration
0.612022
Accelerating Continuous Integration by Caching Environments and Inferring Dependencies · IEEE Trans. Software Eng. 2022

Methods — techniques the papers use, named apart from their topics

dependency inference · 0.6build environment caching · 0.6
YearPublicationVenuePosition
2022 Accelerating Continuous Integration by Caching Environments and Inferring Dependencies
abstract
To facilitate the rapid release cadence of modern software (on the order of weeks, days, or even hours), software development organizations invest in practices like Continuous Integration (CI), where each change submitted by developers is built (e.g., compiled, tested, linted) to detect problematic changes early. A fast and efficient build process is crucial to provide timely CI feedback to developers. If CI feedback is too slow, developers may switch contexts to other tasks, which is known to be a costly operation for knowledge workers. Thus, minimizing the build execution time for CI services is an important task. While recent work has made several important advances in the acceleration of CI builds, optimizations often depend upon explicitly defined build dependency graphs (e.g., make, Gradle, CloudBuild, Bazel). These hand-maintained graphs may be (a) underspecified, leading to incorrect build behaviour; or (b) overspecified, leading to missed acceleration opportunities. In this paper, we proposeKotinos—a language-agnostic approach to infer data from which build acceleration decisions can be made without relying upon build specifications. After inferring this data, our approach accelerates CI builds by caching the build environment and skipping unaffected build steps.Kotinosis at the core of a commercial CI service with a growing customer base. To evaluateKotinos, we mine 14,364 historical CI build records spanning three proprietary and seven open-source software projects. We find that: (1) at least 87.9 percent of the builds activate at least oneKotinosacceleration; and (2) 74 percent of accelerated builds achieve a speed-up of two-fold with respect to their non-accelerated counterparts. Moreover, (3) the benefits ofKotinoscan also be replicated in open source software systems; and (4)Kotinosimposes minimal resource overhead (i.e.,$<$1 percent median CPU usage, 2 MB – 2.2 GB median memory usage, and 0.4 GB – 5.2 GB median storage overhead) and does not compromise build outcomes. Our results suggest that migration toKotinosyields substantial benefits with minimal investment of effort (e.g., no migration of build systems is necessary).
Keheliya Gallaba, John Ewart, Yves Junqueira, Shane McIntosh
IEEE Trans. Software Eng.2