A Vision for Context-Aware CI Adoption Decisions

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 characteristics, and provides configuration guidance tailored to project needs. We outline a research agenda combining developer studies, large-scale repository mining, and recommendation system design to enable informed CI adoption decisions and prevent inefficiencies before they occur.