system integration

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 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.

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 inefficiency in generating and sharing new capabilities for AI agents, stemming from a lack of reusable skills during runtime. To overcome this, we propose a demand-driven, agent-centric skill production platform that introduces a novel “demand-first” paradigm for skill generation. The platform natively integrates full lifecycle skill management into Git workflows, enabling collaborative development, review, and version control among humans, scripts, and external agents within a unified state space. Leveraging mechanisms such as scoped push URLs, range-based commit ingestion, workflow state reading, and event tracing—combined with hosted repositories and registries—the system ensures auditability, recoverability, and multi-interface access (Web/REST/MCP) to skills. Empirical validation demonstrates end-to-end execution of an OS detection skill, conversion of Docker research bundles into reusable skills, and versioned submission of high-quality skill artifacts.

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 a critical limitation in existing cloud skill testing, which focuses solely on task success rates and fails to reveal uncovered behaviors, leaving test adequacy unquantifiable. To bridge this gap, the study introduces the first formal definition of test coverage units, coverage relationships, and a computational pipeline for cloud skills. It proposes a coverage evaluation method grounded in natural language skill packages: by parsing user prompts and initial resource states, the approach reconstructs operational obligations and models workflow context to establish an end-to-end measurement pipeline. Integrating model-assisted candidate generation with expert review, the framework produces auditable coverage reports and closes the loop by mapping coverage gaps to source-level test improvement recommendations. Empirical results demonstrate that this methodology enables quantitative assessment of test coverage for production-grade cloud skills, substantially enhancing their reliability and observability.

This work addresses the reliability degradation of AI agents when reusing workflows due to environmental drift, task ambiguity, or distributional shift. To mitigate these challenges, the authors propose a selective formalization and gated execution mechanism that dynamically determines whether each workflow step should be executed in code or natural language. The approach integrates verification gates, fallback pathways, and multimodal evidence—including outputs, screenshots, and error logs—to enable auditable, versioned, and adaptive workflow governance. Evaluated on WebArena-Verified, the method achieves a single-turn success rate of 53.7%, retains 91.7% of initially successful tasks after up to three retries, and exhibits a low regression rate of only 4.2% post-repair. It also significantly outperforms baseline approaches in cross-site, cross-domain, and GitLab migration tasks.

Current LLM agents lack explicit dependency management for their skills, leading to redundant dependencies, environmental inconsistencies, and security risks. This work proposes the Agent Skill Supply Chain (ASSC) framework, which introduces a systematic model of hybrid dependencies among skills, packages, and services. Inspired by Software Bill of Materials (SBOM), we design SkillDepAnalyzer—a tool that automatically extracts dependency evidence from natural language descriptions to construct skill dependency graphs. Experiments on the SKILL-DEP benchmark demonstrate that our approach significantly outperforms both LLM-based baselines and conventional SBOM tools. An analysis of 1.43 million skills reveals four distinct dependency patterns and enables effective identification and reporting of malicious skills, offering a novel paradigm for secure governance in skill ecosystems.

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.

Automatically constructing high-quality, reusable skills from heterogeneous, fragmented interaction traces—often missing critical security behaviors—is highly challenging. This work proposes the W2S framework, which introduces a novel intermediate representation called RWSA to decouple skills into workflow structure, execution semantics, and runtime attachments, thereby enabling task decomposition, control-flow modeling, verification, rollback, and state management. W2S achieves efficient skill construction through trajectory segmentation, local skill draft generation, structural alignment, branch fusion, redundancy compression, and confidence-aware retention. Experimental evaluation across 70 skills demonstrates that W2S improves behavioral replay consistency by 10.5% compared to baseline approaches based on summarization and prompting.