一句话：结合实证研究与学习型方法，提升 GitHub Actions 工作流的可靠性、安全性与自动生成质量。
CI/CD 管道是现代软件工程的核心组成部分，自动执行开发生命周期的关键阶段。自 2019 年发布以来，GitHub Actions（GHA）迅速成为 GitHub 上主流的 CI/CD 平台，每天自动化数百万条工作流。然而随着生态扩张，开发者在自动创建 GHA 工作流、缓解工作流失败以及保障安全性方面持续面临挑战。该博士研究系统考察开发者实际如何构建、维护与保护 GHA 工作流，并提出基于学习的方法来提升其自动化质量。研究包含两项实证工作：一项关注工作流可靠性（重跑与 flakiness），另一项关注安全（开发者实践的演化）；此外还提出两个基于学习的框架：CIGAR 用于推荐可复用的 Action，FLOWSTEP 用于生成感知步骤的工作流。二者共同指向一个整合可靠性、安全性与语义理解的智能且安全的 GitHub Actions 自动化框架。
CI/CD pipelines form a core component of contemporary software engineering, automating key stages of the development lifecycle. Since its release in 2019, GitHub Actions (GHA) has rapidly emerged as the dominant CI/CD platform on GitHub, automating millions of workflows daily. However, as the ecosystem grows, developers face persistent challenges in automating GHA workflow creation, mitigating workflow failures, and ensuring security. To address these challenges, my doctoral research systematically examines how developers construct, maintain, and secure GHA workflows in practice, and proposes learning-based approaches to improve their automation quality. The research includes two empirical studies on workflow reliability (reruns and flakiness) and security (evolution of developers’ practices), along with two learning-based frameworks: CIGAR for recommending reusable Actions and FLOWSTEP for generating step-aware workflows. Together, they aim to build an intelligent and secure automation framework for GitHub Actions integrating reliability, security, and semantic understanding.