MétaCan
Menu
Back to cohort
Record W4312614778 · doi:10.1109/tse.2022.3222160

Studying the Interplay Between the Durations and Breakages of Continuous Integration Builds

2022· article· en· W4312614778 on OpenAlex
Taher A. Ghaleb, Safwat Hassan, Ying Zou

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Software Engineering · 2022
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsQueen's UniversityUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceContext (archaeology)World Wide WebData scienceInformation retrieval

Abstract

fetched live from OpenAlex

The Continuous Integration (CI) practice allows developers to build software projects automatically and more frequently. However, CI builds may undergo long build durations or frequent build breakages, which we refer to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">build performance</i> . Both long durations and frequent breakages of CI builds can impede developers from engaging in other development activities. Prior research has conducted independent studies on build durations or build breakages. However, there is little attention to the possible interplay between reducing build durations and build breakages. In particular, it is unclear from prior studies (i) whether and how build performance is influenced by the context of projects; (ii) whether the actions to reduce build durations would reduce or increase build breakages; and (iii) whether fixing build breakages would lead to longer or faster builds. It is important for developers to understand the practices that make both timely and passing CI builds. In this paper, we conduct experimental and survey studies on the practices that can have dual or inverse associations with two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">build performance</i> measures: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">build durations</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">build breakages</i> . To this end, we extend an existing dataset called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TravisTorrent</small> to exclude inactive projects and collect recent builds of active projects. As a result, we study 924,616 CI builds from 588 <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GitHub</small> projects that are linked with <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Travis CI</small> . In addition, we survey developers who contributed to the projects in our dataset to get their feedback on our experimental observations. First, we investigate project-level metrics and find that project characteristics have a significant association with build durations and breakages. In addition, we investigate how build-level metrics are associated with both build durations and breakages and observe an evident interplay between them. In particular, we observe that actions to fix build breakages (e.g., retrying or waiting for build commands) not only increase build durations but also do not guarantee passing builds. We also find that improving the build performance of a project is dependent on the current build durations and breakages of that project. Furthermore, we analyze how build performance changes over time and observe nearly a third of projects in which one performance measure is sacrificed in favor of the other, especially when not possible to achieve both together. The majority of our experimental observations are confirmed by survey results, which provide useful insights though some survey responses disagree with some of our experimental observations. Our work (a) provides developers with development and building practices to maintain timely and passing CI builds, and (b) encourages researchers to highlight any potential dual or inverse side effects when reporting actionable findings about CI builds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.256
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it