A Vision for Context-Aware CI Adoption Decisions
Osamah H. Alaini, Taher A. Ghaleb

TL;DR
This paper proposes an AI-driven framework to help software projects make informed, context-aware decisions about adopting Continuous Integration, aiming to improve adoption effectiveness and reduce inefficiencies.
Contribution
It introduces a novel AI-enabled approach for assessing project suitability for CI and recommending tailored CI solutions based on project characteristics.
Findings
Framework outlines assessment and recommendation processes
Research agenda includes developer studies and repository mining
Aims to prevent inefficient CI adoption practices
Abstract
Continuous Integration (CI) is widely adopted in modern software development, yet adoption decisions are often made without systematic consideration of project context. Platforms such as GitHub Actions lower the barrier to CI adoption but provide limited support for grounding adoption decisions in project characteristics, leading to redundant services, unmaintained workflows, and costly migrations. Existing research and tooling primarily focus on improving CI after adoption, offering little guidance for assessing suitability before adoption. As a result, CI is frequently treated as universally beneficial rather than context-dependent. This paper envisions a shift from default CI adoption to deliberate, context-aware decision-making. We propose an AI-enabled framework that assesses whether projects are likely to benefit from CI, recommends suitable CI services based on project…
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