How Developers Adopt, Use, and Evolve CI/CD Caching: An Empirical Study on GitHub Actions
- URL: http://arxiv.org/abs/2604.13129v1
- Date: Mon, 13 Apr 2026 21:51:43 GMT
- Title: How Developers Adopt, Use, and Evolve CI/CD Caching: An Empirical Study on GitHub Actions
- Abstract summary: Continuous Integration/Continuous Delivery (CI/CD) caching is widely used to reduce repeated computation and improve CI/CD efficiency.<n>We present the first empirical study on how developers configure and evolve caching in CI/CD on GitHub Actions.
- Score: 4.2004109000690715
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Continuous Integration/Continuous Delivery (CI/CD) caching is widely used to reduce repeated computation and improve CI/CD efficiency, yet maintaining effective caching requires ongoing maintenance effort. In this paper, we present the first empirical study on how developers configure and evolve caching in CI/CD workflows on GitHub Actions. We analyze 952 GitHub repositories (266 cache adopters and 686 non-adopters), to compare repository characteristics, characterize caching usage at the job and step levels, uncover patterns in caching configuration evolution, and identify the drivers of cache-related changes. Our analysis spans 1,556 workflow files, 10,373 commits, and 17,185 workflow configuration changes, including an average of 9.37 cache-related changes per repository. Our main observations are: (1) cache-adopting repositories are more active and popular than non-adopters; (2) caching is used across multiple CI/CD job types through a variety of caching mechanisms rather than a single standardized approach; (3) caching configurations evolve through frequent, repetitive maintenance patterns, with rapid updates in build and test jobs and slower evolution in other job types; and (4) cache-related modifications are driven by distinct maintenance needs: parameter updates are mainly human-driven to fix issues, while version updates occur later and are often bot-driven for dependency maintenance. Our findings quantify the substantial maintenance effort involved in CI/CD caching and highlight opportunities to improve reliability and tool support.
Related papers
- Recency/Frequency Adaptive KV Caching for Large Language Model Serving [7.261125311665803]
Key-value ( KV) caching is a powerful technique for accelerating large language model inference and generation.<n>Inference workloads are large and diverse, which makes them difficult to cache effectively.<n>We integrate adaptive caching that dynamically allocates cache space between recently and frequently occurring KV blocks.
arXiv Detail & Related papers (2026-06-19T09:05:01Z) - Decouple and Cache: KV Cache Construction for Streaming Video Understanding [53.55135022958052]
Streaming video understanding requires processing unbounded video streams with limited memory and computation.<n>We propose Decoupled Streaming Cache(DSCache), a training-free cache construction mechanism that adapts pretrained offline models to streaming settings.<n> Experiments on Streaming Video QA benchmarks demonstrate DSCache's state-of-the-art performance, with an average 2.5% accuracy gains over prior methods.
arXiv Detail & Related papers (2026-05-03T13:02:44Z) - Cache-Related Smells in GitLab CI/CD: Comprehensive Catalog, Automated Detection, and Empirical Evidence [1.6312989763677892]
We present a comprehensive catalog of ten cache-related smells in GitLab CI/CD.<n>To address the smells, we propose CROSSER, a tool that automatically detects seven of the ten smells.
arXiv Detail & Related papers (2026-04-20T07:02:36Z) - The Promise and Reality of Continuous Integration Caching: An Empirical Study of Travis CI Builds [0.5951328229856059]
Continuous Integration (CI) provides early feedback by automatically building software.<n>CI services offer caching mechanisms to speed up builds by reusing infrequently changing artifacts.<n>We analyze 513,384 builds from 1,279 GitHub projects.
arXiv Detail & Related papers (2026-01-27T03:23:19Z) - DiCache: Let Diffusion Model Determine Its Own Cache [62.954717254728166]
DiCache is a training-free adaptive caching strategy for accelerating diffusion models at runtime.<n>Online Probe Profiling Scheme leverages a shallow-layer online probe to obtain an on-the-fly indicator for the caching error in real time.<n> Dynamic Cache Trajectory Alignment approximates the deep-layer feature output from multi-step historical caches.
arXiv Detail & Related papers (2025-08-24T13:30:00Z) - LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models [52.56008278458534]
LaCache is a training-free method for efficient and accurate generative inference of Large Language Models.<n>LaCache enables LLMs to address both of the critical challenges in long-range modeling: robust long-range capabilities and continuous generation without running out-of-memory.
arXiv Detail & Related papers (2025-07-14T19:09:57Z) - Efficient Unified Caching for Accelerating Heterogeneous AI Workloads [20.192896983810606]
Modern AI clusters host diverse workloads like data pre-processing, training and inference.<n>To avoid code-intrusion complexity and minimize cache space wastage, it is desirable to maintain a unified cache shared by all the workloads.<n>We propose IGTCache, a unified, high-efficacy cache for modern AI clusters.
arXiv Detail & Related papers (2025-06-14T06:36:54Z) - dKV-Cache: The Cache for Diffusion Language Models [53.85291644298835]
Diffusion Language Models (DLMs) have been seen as a promising competitor for autoregressive language models.<n>We propose a KV-cache-like mechanism, delayed KV-Cache, for the denoising process of DLMs.<n>Our approach is motivated by the observation that different tokens have distinct representation dynamics throughout the diffusion process.
arXiv Detail & Related papers (2025-05-21T17:32:10Z) - EPIC: Efficient Position-Independent Caching for Serving Large Language Models [19.510078997414606]
Caching improves serving performance by reusing Key-Value vectors across requests.<n>Existing context caching requires exact prefixes across requests.<n>We introduce Position-Independent Caching (PIC), which enables modular reuse of KV vectors regardless of prefixes.<n>We also introduce EPIC, a serving system incorporating our new LegoLink algorithm, which mitigates the inappropriate "attention sink" effect at every document beginning.
arXiv Detail & Related papers (2024-10-20T08:42:29Z) - Efficient Inference of Vision Instruction-Following Models with Elastic Cache [76.44955111634545]
We introduce Elastic Cache, a novel strategy for efficient deployment of instruction-following large vision-language models.
We propose an importance-driven cache merging strategy to prune redundancy caches.
For instruction encoding, we utilize the frequency to evaluate the importance of caches.
Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation.
arXiv Detail & Related papers (2024-07-25T15:29:05Z) - Reinforcement Learning for Caching with Space-Time Popularity Dynamics [61.55827760294755]
caching is envisioned to play a critical role in next-generation networks.
To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache.
This chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design.
arXiv Detail & Related papers (2020-05-19T01:23:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.