Context-Aware Behavior-Driven Pipeline Generation

Abstract

An efficient CI/CD process is crucial for modern software teams, but manual pipeline creation is error-prone and requires high DevOps expertise, slowing deployment speed and reducing productivity. This research introduces a context-aware, behavior-driven approach to fully automating CI/CD pipeline generation by analyzing GitHub user activity patterns. The proposed solution utilizes a historical analysis of repository events, developer contributions, and workload distribution to dynamically generate pipelines and assign reviewers to pull requests based on expertise. Unlike previous template-based and generative AI solutions that require manual intervention, our approach leverages pattern recognition and adaptive decision-making to continuously refine automation. This paper presents the methodology behind data collection, analysis, and pipeline generation, demonstrating its effectiveness in reducing human effort while improving software delivery efficiency. This research highlights how behavior-driven automation streamlines the complexity of CI/CD pipeline creation, enabling more adaptive and intelligent systems that effectively respond to the evolving needs of software development teams.