ansible

This study identifies four core challenges in Ansible’s Infrastructure-as-Code (IaC) practice: performance bottlenecks, flawed abstraction design, weak debugging and error diagnosis capabilities, and insufficient documentation and learning resources. Employing a mixed-methods empirical approach—quantitative text mining of 59,157 community forum posts and qualitative analysis of 16 in-depth practitioner interviews—it provides the first evidence-based characterization of the real-world engineering costs incurred by the “Worse is Better” philosophy in IaC tooling. The work proposes a four-dimensional improvement framework targeting maintainability, understandability, debuggability, and evolvability, yielding four actionable design recommendations—several of which have been adopted by the Ansible Core Team. These findings establish a critical empirical benchmark for advancing IaC tool design, DevOps education, and open-source community support.

This study addresses the challenges of high latency, unstable concurrency, and security risks faced by large language model (LLM) agents in automating asset lifecycle management within Industry 4.0. The authors propose a Plan-then-Execute architecture that generates verifiable workflow graphs and integrates a topology-aware parallel scheduling mechanism to enable controlled inference overlap while ensuring functional correctness and security. Key technical contributions include topological-sort-based multi-agent scheduling, structured context pruning, dependency-aware concurrency control, and graceful degradation under fault injection. Evaluated on the AssetOpsBench benchmark, the system reduces median end-to-end latency by 1.6× (up to 1.8× for highly parallel tasks) and cuts inference overhead by approximately 30% through context pruning, all while maintaining stable task completion rates and output quality.

The rapid proliferation of Infrastructure-as-Code (IaC) scripts—particularly Ansible playbooks—lacks systematic, scalable quality assessment methodologies. Method: This paper proposes the first extensible, multi-dimensional, and quantifiable IaC code quality assessment framework. Leveraging over one thousand real-world repositories from Ansible Galaxy, it integrates static analysis, metadata mining, and empirical study to construct a weighted evaluation model across dimensions including error handling, automation level, and documentation completeness. Temporal analysis further uncovers evolutionary trends—e.g., progressive metadata improvement alongside declining automation capability. Contribution/Results: The framework establishes a theoretical foundation for IaC quality standardization and enables practitioners to precisely identify quality bottlenecks, thereby facilitating engineering-driven quality governance in IaC development and maintenance.

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 work addresses the lack of empirical evaluation of large language models’ (LLMs) ability to generate executable IT automation scripts—particularly for Ansible. We introduce ITAB, the first real-world benchmark comprising 126 state-calibrated tasks, and pioneer state reconciliation as a core evaluation dimension. Using dynamic execution validation, failure attribution analysis, and Ansible sandbox testing, we conduct a horizontal evaluation of 14 open-source LLMs. Results show a maximum pass@10 of only 12%, with two dominant error categories identified: state reasoning failures (44.87%) and deficits in Ansible module–specific knowledge (24.37%). The study reveals that state tracking and domain-specific execution understanding constitute critical bottlenecks for LLMs in IT automation. Our findings provide empirically grounded insights and methodological foundations for both LLM improvement and future benchmark development.

This work addresses the challenge of highly manual and non-generalizable environment configuration in repository-level software engineering tasks by introducing RAT, the first language-agnostic framework for fully automated repository setup. RAT establishes an end-to-end pipeline comprising semantic initialization, task planning, invocation of specialized tools, and robust sandbox construction. To evaluate such systems realistically, the authors also release RATBench, the first benchmark reflecting the true distribution and heterogeneity of real-world code repositories. Experimental results demonstrate that RAT significantly outperforms strong existing baselines on RATBench, achieving an average 29.6% improvement in Environment Setup Success Rate (ESSR). This advance overcomes prior limitations that relied on predefined artifacts or were confined to specific programming languages.

This work addresses the limitations of traditional workflow platforms, which rely on static, pre-defined processes and struggle to accommodate the dynamic data integration demands of distributed systems. To overcome this, the authors propose a configuration-driven runtime orchestration framework that dynamically constructs execution graphs at request time through dependency-aware scheduling and parallel task execution, thereby circumventing the constraints of fixed workflows. This approach enables rapid adaptation to evolving integration scenarios without requiring system redeployment, significantly reducing latency. Empirical evaluation in a real-world Customer 360 enterprise use case demonstrates that the framework offers substantial advantages in flexibility, scalability, and efficient data aggregation compared to conventional solutions.

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.

This work addresses the lack of structured, verifiable, and reusable decision mechanisms in existing automated machine learning approaches for model selection. It proposes a semantic task profiling–based structured agent framework that leverages retrieval-augmented generation of historical cases and code modules to construct an intermediate representation blueprint encompassing modeling components, composition logic, and execution constraints. By integrating code execution feedback with a failure-aware reinforcement learning strategy, the framework enables memory-driven, traceable, multi-stage search optimization. Evaluated on financial time-series forecasting and generation tasks, the method significantly outperforms both conventional AutoML systems and current agent-based baselines, achieving consistent improvements in task performance, execution success rate, and decision interpretability.

This work addresses the fragmentation in existing frameworks that treat deterministic and probabilistic computations in isolation, lacking a unified declarative language to orchestrate large language models (LLMs) and symbolic tools. We propose Structured Prompt Language (SPL), the first framework to deeply integrate probabilistic operations (GENERATE/EVALUATE) and deterministic reasoning (SOLVE/ASSERT) within a single declarative paradigm. SPL supports shared variable binding, runtime dynamic routing, and seamless interoperability with LLMs (e.g., Ollama, Anthropic), symbolic engines (e.g., SymPy, SageMath, Lean), and the distributed execution grid Momagrid. Across 1,200 experiments, SPL achieves machine-verified correctness rates of 82–93% (e.g., 93% for gemma4:e2b), substantially outperforming pure LLM baselines; most failures stem from solver kernels rejecting invalid expressions.