deployment

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 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.

Configuration discrepancies between software development and production environments frequently cause behavioral inconsistencies, recurrent failures, and unplanned downtime. To address this, we conducted in-depth interviews with 17 industry experts and applied thematic analysis to systematically identify key pain points in configuration governance. Based on empirical evidence, we propose the first structured, practice-oriented taxonomy of mitigation strategies—comprising eight actionable categories: process design, automated deployment, Infrastructure-as-Code (IaC) adoption, Docker-based containerized validation, virtualization-based environment isolation, and closed-loop verification, among others. This taxonomy bridges a critical methodological gap in industrial configuration drift management, significantly enhancing environment consistency, incident response efficiency, and regulatory compliance assurance. The framework has been successfully implemented and validated across multiple enterprise DevOps pipelines.

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.

Continuous Integration (CI) practices suffer from severe monitoring deficiencies: developers largely neglect critical metrics such as “build health” and “time-to-fix failed builds,” while mainstream CI services offer only weak native monitoring capabilities, forcing reliance on fragmented and often redundant third-party tools. Method: We conducted a triangulated investigation—including documentation analysis, developer surveys, functional audits of CI platforms, and case studies of open-source projects—to systematically identify cognitive gaps and practical monitoring needs. Contribution/Results: Our study provides the first empirical evidence that although over 80% of developers track test coverage, only a minority monitor build health or timeliness; further, all major CI services lack built-in multidimensional monitoring support. These findings establish an evidence-based foundation for designing next-generation CI monitoring frameworks and prioritizing tooling enhancements.

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.

While large language models (LLMs) can generate executable multi-service application environments, they often deviate from the architectural and security requirements essential for production deployment. This work proposes a method to automatically generate Dockerfiles and Docker Compose configurations solely from code repositories, evaluating deployment fidelity through end-to-end HTTP testing and structural comparison. It explicitly distinguishes between functional correctness and fidelity to deployment intent, deriving a minimal set of explicit deployment specifications that cannot be inferred automatically from source code alone. Experiments successfully reproduce the topology and dependencies of three heterogeneous multi-service systems, confirming functional feasibility; however, critical production-grade features—such as network isolation and multi-stage builds—are consistently absent, revealing fundamental limitations in current LLMs’ ability to model deployment intent.

This work addresses the unreliability of developer productivity dashboards, which often stems from ad hoc scripts that introduce undetected silent data gaps, eroding organizational trust. To resolve this, we propose a robust ELT pipeline grounded in DAG-based orchestration and the Medallion architecture, decoupling data extraction from transformation to preserve the immutability of raw data. Our approach introduces a state-driven dependency scheduling mechanism and, for the first time, treats metric pipelines as production-grade distributed systems. We emphasize the critical role of immutable raw history in enabling reliable metric redefinition. This methodology significantly enhances data reliability and freshness while effectively eliminating silent failures, thereby restoring organizational confidence in DevOps metrics.