jenkins

Traditional Jenkins controllers suffer from resource overloading and reduced reliability due to direct execution of build tasks. To address this, we propose a lightweight CI/CD architecture that containerizes the Jenkins controller and offloads all build execution to remote Docker hosts via secure SSH connections—effectively decoupling orchestration from build execution. The architecture incorporates atomic deployments, timestamped artifact backups, immutable artifact packaging, and automated notification mechanisms. Technically, it integrates persistent volumes, containerized build environments, and declarative pipelines. Experimental evaluation demonstrates a significant reduction in controller CPU and memory utilization, a 32% increase in build throughput, and a 41% decrease in artifact delivery latency. The solution delivers high stability, scalability, and low operational overhead, making it particularly suitable for small- to medium-scale DevOps environments.

This work addresses the challenge developers face in efficiently authoring CI/CD configurations due to limited DevOps expertise by proposing a large language model (LLM)-based, context-aware generation approach. The method leverages both natural language descriptions and repository structure to automatically produce accurate and executable pipeline configurations for platforms such as GitHub Actions and GitLab CI/CD. Integrated with automated validation and human-in-the-loop feedback mechanisms, this framework is the first to combine repository context understanding with natural language-driven configuration synthesis. Experimental results demonstrate that the approach significantly lowers the barrier to DevOps adoption, markedly improves the accuracy and validity of generated configurations, and substantially reduces manual configuration effort.

To address the challenges of prolonged CI pipeline deployment cycles, error-prone manual configuration, and poor cross-project consistency, this paper proposes an automated pipeline configuration framework grounded in Infrastructure-as-Code (IaC) principles and templated configuration. The framework enables declarative definition and one-click generation of CI/CD pipelines via reusable YAML templates, a parameterized pipeline engine, and an integrated automation toolchain. Compared to conventional manual approaches, our method reduces average pipeline deployment time by 72% and decreases human configuration errors by 91%, while substantially improving consistency in build logic and execution environments across projects. Empirical validation across six open-source projects demonstrates the framework’s engineering practicality and methodological generality. It provides a reusable implementation model and actionable methodology for CI/CD automation, advancing scalable, maintainable, and reproducible software delivery practices.

This work addresses the fragility, inefficiency, and strong platform coupling commonly found in CI/CD pipelines for legacy COBOL systems, which often result in high maintenance costs and vendor lock-in. To overcome these challenges, the authors propose a portable CI/CD architecture tailored for highly secure and compliance-driven environments. The approach leverages OCI-compliant container images preloaded with COBOL toolchains, introduces a platform abstraction layer, integrates multiple repositories, and employs Groovy script refactoring to achieve platform-agnostic continuous integration and delivery. Empirical evaluation demonstrates that the proposed solution significantly enhances efficiency—reducing pipeline execution time by 82%—while simultaneously improving system portability, security, and maintainability. This architecture offers a reusable paradigm for modernizing legacy COBOL applications within regulated domains.

This study presents the first empirical investigation into the evolution of CI/CD configurations in machine learning (ML) projects. Addressing the lack of understanding regarding how CI/CD configurations co-evolve with ML components, the authors analyze 508 open-source ML projects, 343 manually annotated commits, and 15,634 automated CI/CD commits. They propose a novel 14-category taxonomy capturing synergistic changes between CI/CD and ML components, develop a dedicated clustering tool to identify recurrent evolutionary patterns, and establish an empirically grounded model linking developer experience to CI/CD configuration modification behavior. Results show that 61.8% of CI/CD-related commits involve build strategy modifications; common anti-patterns—including dependency hardcoding and missing test frameworks—are identified; and senior developers modify CI/CD configurations more frequently and effectively than juniors, confirming the critical role of experience in CI/CD maintenance.

This study presents the first large-scale analysis of AI agents’ modifications to CI/CD configurations, examining 8,031 pull requests authored by AI agents—such as Copilot, Codex, and Devin—across 1,605 GitHub repositories, with a focus on YAML-based CI/CD workflow files. The findings reveal that CI/CD-related changes constitute 3.25% of all AI-generated modifications, with 96.77% targeting GitHub Actions. While the overall build success rate for AI-authored changes (75.59%) is comparable to that of human-authored ones (74.87%), their merge rate is slightly lower. Notably, Copilot demonstrates a significantly higher merge rate (+15.63 percentage points) in CI/CD tasks, suggesting emerging specialization among AI agents in automated DevOps workflows and highlighting behavioral differences across agent types in infrastructure automation contexts.

This work addresses the inefficiency of traditional static CI/CD workflows in adapting to varying commit risks during system scaling. It introduces the first formulation of CI/CD pipelines as a Markov decision process and proposes a reinforcement learning–based dynamic test scheduling approach that enables runtime-adaptive test selection. The method significantly enhances pipeline efficiency while maintaining high defect detection quality, achieving a defect miss rate below 5%. Compared to static baselines, it improves throughput by up to 30% and reduces test execution time by approximately 25%. This study establishes the first reinforcement learning–driven dynamic decision framework for optimizing CI/CD pipelines, offering a principled and scalable solution to adaptive testing in continuous integration environments.

Existing LLM agent systems suffer from tight coupling between logical workflows and underlying programming languages or deployment environments, resulting in high development/deployment complexity and poor maintainability. This paper proposes a declarative domain-specific language (DSL) tailored for LLM agent workflows—marking the first effort to universally abstract and unify common patterns such as RAG, API orchestration, and filtering. The DSL fully decouples workflow specification from execution semantics, enabling cross-language (Java/Python/Go) and cross-environment (cloud-native/on-premises) deployment. The system integrates multi-backend adapters, a lightweight workflow engine, and an automated metrics collection framework, natively supporting multi-strategy A/B testing and performance benchmarking. Evaluated in PayPal’s e-commerce setting, it reduces development time by 60% and accelerates deployment threefold; complex workflows shrink from >500 to <50 lines of code, achieve orchestration latency under 100 ms, and allow safe, non-engineer configuration.

This work addresses the absence of a unified conceptual framework for describing the autonomy of AI agents and the allocation of decision-making authority in contemporary CI/CD pipelines. It introduces the notion of “authority transfer” to systematically delineate the boundaries of agent autonomy, distinguishing between decision rights in the data plane and the control plane, and identifies governance of the control plane as a critical research direction. Through architectural abstraction, pattern identification, and governance mechanism design—supported by prototype implementation and analysis of industrial platforms—the study reveals three prevalent patterns: constrained autonomy, externally dominated governance, and delayed evaluation. These findings establish a theoretical foundation and outline a research agenda for developing safe, controllable, and highly autonomous CI/CD systems.

This work addresses critical challenges in configuration management for large language model (LLM)-based coding agents, including configuration reuse ambiguities, unclear permission boundaries, and inadequate versioning. To tackle these issues, the authors propose Rel(AI)Build—the first deterministic, tool-agnostic configuration governance framework specifically designed for LLM coding agents. Treating agent definitions as managed supply chains, Rel(AI)Build enforces configuration integrity through SHA-256 content addressing, HMAC-signed lockfiles, hash-chain audit logs, hierarchical access controls, and a state-machine-driven development workflow. The framework also supports multi-IDE target compilation. Empirical evaluation demonstrates that Rel(AI)Build effectively preserves configuration immutability under adversarial compliance tests, thereby validating its reliability and security guarantees.