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 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 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 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.

This study addresses the persistent challenge organizations face in aligning DevOps automation initiatives with strategic objectives such as waste reduction, delivery predictability, cross-team collaboration, and customer-perceived quality. To bridge this gap, the authors propose a unified VSM–GQM–DevOps framework that integrates Value Stream Mapping (VSM), the Goal-Question-Metric (GQM) approach, and DevOps practices. The framework enables identification of delivery bottlenecks, construction of decision-oriented measurement models, and implementation of maturity-aligned, reversible automation interventions, thereby establishing an auditable and traceable pathway for automation investment. Validated through a multi-site longitudinal study employing DORA metrics, interrupted time series analysis, and mixed-methods evaluation, the framework demonstrates significant improvements in delivery performance and project management outcomes, fostering continuous, strategy-aligned improvement.

This work addresses the lack of closed-loop control in traditional software development lifecycles, which often fails to simultaneously ensure security, auditability, and highly reliable automation. The authors propose a deterministic autonomous control framework that models the lifecycle as a seven-stage automated pipeline, integrating Jira-based task orchestration, structured context, resource constraints, and human-review gating mechanisms to establish a secure closed loop. Key innovations include a state-contract-based collision locking mechanism, a degradation protocol for fallback operation, and a traceable control architecture. Implemented with 12,661 lines of Python code and 6,907 lines of versioned prompt specifications—including 101 exception handlers and 12 centralized locks—the system achieved a 100% success rate (95% CI [97.6%, 100%]) across 152 initial runs, producing over 795 artifacts. All 51 issues identified through adversarial review were fully resolved, with 60% of security tickets autonomously completed.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

Traditional engineering analysis automation relies on fixed workflows and rigid interfaces, making it ill-suited to accommodate changes in data formats, units, or methodologies driven by product evolution. This work proposes DUCTILE, a novel framework that introduces large language model (LLM) agents into engineering analysis automation for the first time. By decoupling adaptive task orchestration from deterministic tool execution, DUCTILE dynamically interprets design documentation and adjusts processing pipelines under engineer supervision. The approach ensures both regulatory compliance and robustness while supporting structured document parsing, seamless tool integration, and human oversight. Evaluated on structural analysis tasks in aerospace manufacturing, DUCTILE successfully handled input variations, consistently producing results meeting expert standards across multiple independent runs and demonstrating practical viability through deployment by frontline engineers.

This study addresses the open challenge of effectively integrating generative AI into safety-critical, resource-constrained embedded software engineering while meeting stringent requirements for determinism, reliability, and traceability. Through semi-structured focus group interviews and structured brainstorming sessions with ten senior experts from four industry partners, the research systematically identifies eleven emerging practices and fourteen key challenges centered on the orchestration, governance, and integration of generative AI tools. The findings reveal how embedded development teams are strategically reconfiguring their workflows, roles, and toolchains to accommodate AI augmentation. This work provides the first empirical foundation and a sustainable, agent-oriented pipeline transformation pathway for deploying generative AI in safety-critical development contexts.