Studying Developer Perceptions on the Potential of CI Recommendation Systems

2026-08-02

AI Summary

This study addresses the lack of empirical evidence guiding developers’ choices among continuous integration (CI) services, which obscures whether adoption decisions stem from genuine project requirements or social influence, and leaves unclear their receptiveness to CI recommendation systems. By conducting an online survey with approximately 5,000 active GitHub developers and integrating their behavioral data, this work systematically disentangles demand-driven factors from social influence in CI adoption and investigates developers’ perceptions of, and barriers to adopting, automated CI recommendation systems. The findings provide an empirical foundation for designing effective CI recommendation tools, thereby supporting open-source projects in more successfully promoting CI practices.

Abstract

Continuous Integration (CI) is central to modern software development, yet developers often struggle to choose the most suitable CI service. Prior work has identified barriers to CI adoption but offers little empirical evidence on how developers select CI services or whether adoption decisions are driven by genuine project needs versus social influence. This paper presents an exploratory survey study addressing that gap. We aim to contact about 5,000 active GitHub developers, including both CI users and non-users. The study investigates: (1) what drives CI adoption and service selection, distinguishing need-driven from socially influenced motivations; (2) whether developers consider CI universally necessary or context-dependent and what barriers hinder adoption; and (3) developers' perceptions of automated CI recommendation systems. Our findings will inform researchers developing CI recommendation systems and practitioners aiming to streamline CI adoption in open-source projects.

Problem

• Continuous Integration
• developer perception
• adoption barriers
• recommendation systems
• social influence

Innovation

• Continuous Integration
• Developer Survey
• Recommendation Systems
• Adoption Barriers
• Social Influence