build ci/cd pipelines

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 paper addresses the conceptual ambiguity, ill-defined boundaries, and lack of implementation standards between Infrastructure-as-Code (IaC) and Pipeline-as-Code in DevOps practice. To resolve these issues, we systematically delineate their respective roles and synergistic mechanisms within the DevOps ecosystem and propose a reusable, standardized IaC-driven CI/CD implementation framework. Our approach integrates Terraform for infrastructure provisioning, Ansible for configuration management, GitLab CI for pipeline orchestration, and Docker/Kubernetes for containerized deployment—enabling an end-to-end automated delivery pipeline. Empirical evaluation demonstrates 99.8% configuration change accuracy, reduces environment provisioning time from hours to minutes, and significantly improves deployment consistency and delivery efficiency.

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 addresses the quality-efficiency-cost imbalance in industrial CI/CD pipelines caused by heterogeneous failure types. We propose a process refactoring paradigm centered on two critical milestones: code integration (pre-merge) and product release. First, we systematically define “good failures” (early-detected, low-cost) versus “bad failures” (late-occurring, high-blocking). Grounded in empirical studies across four enterprises—including workflow mapping and failure root-cause modeling—we develop a transferable pre-merge failure governance framework. Evaluation results show a 37% reduction in average feedback latency, a 29% decrease in spurious build overhead, significant improvement in developer throughput, and optimized cloud resource utilization. Our core contribution lies in transcending conventional stage-based pipeline segmentation to enable failure-driven, fine-grained process control—marking a paradigm shift toward adaptive, cost-aware CI/CD orchestration.

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.

This work addresses the growing complexity of CI/CD pipelines and the lack of structured analysis capabilities in existing tools for understanding their behavior, failures, and version evolution. The authors propose an innovative approach that uniquely integrates digital twin technology with BPMN-based modeling in DevOps contexts. By automatically parsing raw CI configurations and execution logs, the method constructs structured, high-level process models that enable pipeline visualization, failure traceability, and cross-version comparison. Evaluated across multiple open-source projects, the approach demonstrates effectiveness in monitoring, evolutionary analysis, and fault diagnosis, offering a modular and extensible foundational framework for the analysis and optimization of CI/CD pipelines.

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 work addresses the limitations of existing CI/CD workflow analyses, which often focus narrowly on stage identification and struggle to assess reliability, maintainability, and optimization priorities. To overcome this, we propose a large language model–based CI/CD analysis pipeline that integrates repository context enhancement, anti-pattern detection, stage mining, and actionable recommendation generation. Our approach uniquely combines diagnostic reasoning, context awareness, and human-in-the-loop review to deliver observability tailored to cybersecurity engineering. Leveraging few-shot prompting, YAML parsing, and statistical tests (chi-square and Cramér’s V), the method identifies 434,769 anti-patterns across 75,201 workflows and generates an average of 8.25 syntactically valid optimization suggestions per repository, achieving a 96.1% compliance rate with YAML syntax standards.

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 study addresses the lack of standardized guidance for effectively integrating technical debt management tools into existing CI/CD practices, which hinders the continuous control of technical debt. By systematically analyzing approximately 600,000 Travis CI configuration files and 50,000 auxiliary scripts from GitHub, the authors identify 3,684 pipelines that integrate technical debt management tools. Their findings reveal that such integrations predominantly rely on external script invocations and frequently exhibit configuration anti-patterns, notably the absence of feedback mechanisms. This work provides empirical evidence of current integration practices and prevalent anti-patterns, offering actionable insights to inform the design of better tooling and improve CI/CD integration strategies for technical debt management.