automation

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 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.

Early-stage software development—spanning requirements elicitation, testing, and deployment—is hindered by ill-defined tasks and dense manual intervention points, impeding automation. Traditional CI/CD pipelines address only post-coding phases, leaving semantic gaps between underspecified stages unbridged. Method: We propose “workflow-as-software,” a novel paradigm that models end-to-end development as programmable workflows. Leveraging large language models (LLMs) as universal semantic adapters, our approach automatically reconciles heterogeneous task semantics. It integrates domain-specific workflow orchestration, a lightweight domain-specific language (DSL), and semantic translation interfaces. Contribution/Results: Evaluated in production at Volvo, the method reduced test automation effort by 2–3 full-time engineers and compressed the end-to-end development-to-deployment cycle to two months. It marks the first demonstration of LLM-driven, fully automated software delivery across the entire lifecycle—from requirements to deployment—thereby extending automation beyond conventional CI/CD boundaries.

This study addresses the significant burden developers face in authoring and maintaining GitHub Actions workflows, stemming from a lack of systematic understanding of real-world automation and reuse practices. Through a mixed-methods approach combining a survey of 419 practitioners with qualitative and quantitative analysis, this work presents the first developer-centric characterization of common automation tasks, patterns of reuse mechanism adoption, and maintenance pain points in workflow development. The findings reveal that while developers heavily rely on reusable Actions, they seldom adopt reusable workflows; version management challenges lead to rampant copy-pasting; and critical aspects such as security and performance monitoring remain under-automated. These insights provide empirical foundations for improving CI/CD toolchains and reuse mechanisms.

To address the insufficient security and scalability of engineering workflow automation and cross-organizational collaboration in Industry 4.0, this paper proposes an engineering workflow management approach integrating Asset Administration Shells (AAS) with BPMN. We innovatively design a distributed, write-on-copy AAS infrastructure to ensure data consistency and access security, and develop a lightweight workflow engine prototype supporting native AAS operations, enabling automatic mapping and execution of BPMN processes onto AAS interactions. This method unifies digital twin representation, asset modeling, and business process logic, thereby significantly enhancing standardization of engineering data exchange, end-to-end process traceability, and multi-stakeholder collaboration efficiency. Experimental evaluation demonstrates the system’s feasibility for secure inter-organizational coordination and its horizontal scalability across heterogeneous industrial environments.

This study addresses the challenge of automating workflows in complex industries—such as logistics, healthcare, and construction—where processes are fragmented across heterogeneous tools and involve multi-party collaboration. The work proposes orchestration as a core abstraction to enable effective automation by dynamically coordinating multi-step tasks, enforcing domain-specific constraints, managing human approvals, and integrating legacy systems. It introduces the novel concept of “orchestration bottlenecks” and develops a theoretical framework that unifies multi-agent systems, workflow modeling, constraint reasoning, and human–AI collaboration, while exposing critical gaps in current multi-agent approaches at the orchestration level. Based on distinct sources of operational friction across domains, the paper advocates for targeted architectural safeguards—such as constraint enforcement or explainability—and phased implementation strategies to provide actionable pathways for automation in complex operational environments.

研究通过分析GitHub Agentic Workflows的结构和维护方式，探讨了开发者如何定义和维护由AI代理执行的工作流程，并建议增加防御措施。

This work addresses the limitations of general-purpose large language models in business process automation, where inconsistent functionality, frequent tool-calling errors, and unstable code quality hinder industrial-grade reliability and maintainability. Focusing on the task of translating BPMN diagrams into executable agent workflows, we propose the first specialized agent system designed for structured code generation. By integrating BPMN control-flow semantics, a deterministic path execution mechanism, and a lightweight code generation strategy, our approach achieves high-precision, low-latency, and zero-repair automation. Experimental results demonstrate that, compared to general-purpose models, our method improves tool-calling accuracy by 9–20 percentage points, reduces latency by 2–4×, decreases calling errors by a factor of three, lowers token-generation costs by over 95%, and entirely eliminates the need for repair iterations.

This study addresses the critical issue of frequent failures in GitHub Actions workflows, which severely undermine automation reliability and maintainability. For the first time, it systematically maps 197 language constructs to 14 workflow capability features through a large-scale quantitative analysis of over 260,000 workflows across 49,000 repositories. By integrating language construct categorization with metadata mining, the work uncovers prevalent usage patterns, evolutionary trends, and their impact on workflow reliability. The findings reveal that only a small subset of constructs is heavily used, and that specific capability features are significantly associated with elevated failure rates and maintenance costs. These empirical insights provide actionable guidance for optimizing workflow design and improving robustness in continuous integration and delivery pipelines.

This work addresses the challenges of constructing and managing generative AI agent systems for long-horizon, stateful, multi-step business processes by proposing a graph-structured workflow design methodology. Leveraging the LangGraph framework, it explicitly models core mechanisms such as state management, conditional routing, and human-in-the-loop interventions. The approach is instantiated in three representative applications: SQL analysis with repair loops, retrieval-augmented generation gated by evidential validation, and human-AI collaborative policy review supporting interruption and checkpoint-based recovery. By treating behaviors like routing, pausing, and audit trails as explicit product features rather than implicit prompt logic, this study not only delineates the applicability boundaries of LangGraph in high-complexity workflows but also substantially enhances system controllability, reliability, and auditability in real-world operational settings, establishing a reusable engineering paradigm.