Accelerating Continuous Integration by Caching Environments and Inferring Dependencies
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.
Bibliographic record
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 propose <small>Kotinos</small> —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. <small>Kotinos</small> is at the core of a commercial CI service with a growing customer base. To evaluate <small>Kotinos</small> , 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 one <small>Kotinos</small> acceleration; 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 of <small>Kotinos</small> can also be replicated in open source software systems; and (4) <small>Kotinos</small> imposes minimal resource overhead (i.e., <inline-formula><tex-math notation="LaTeX">$<$</tex-math></inline-formula> 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 to <small>Kotinos</small> yields substantial benefits with minimal investment of effort (e.g., no migration of build systems is necessary).
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it