configuration management

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.

Automatically generating YAML configuration files that are both structurally valid and compliant with multiple continuous integration (CI) service specifications remains a significant challenge, and the capabilities of current large language models (LLMs) on this task are not well understood. This work introduces DOC2CI, the first cross-CI benchmark dataset comprising 3,363 document–YAML pairs, and systematically evaluates 14 open-source models alongside GPT-series models. A novel failure taxonomy is proposed to uncover the root causes of model discrepancies, and this study provides the first empirical evidence that document similarity and structural validity constitute distinct optimization objectives. Experiments reveal that even the largest models achieve an Exact Match rate below 3.1%; while 97% of generated outputs are syntactically parseable, only 71% conform to the target service schema. Schema-guided post-hoc repair without additional training boosts structural validity to 94%, whereas fine-tuning improves document similarity at the expense of standalone structural correctness.

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.

Despite the widespread use of dotfiles for personal configuration management (e.g., vim, bash/zsh), empirical understanding of how developers share and maintain them remains scarce. Method: This study conducts the first large-scale empirical analysis of publicly available dotfiles repositories on GitHub, leveraging GitHub API-collected metadata and textual analysis, complemented by statistical modeling to quantify configuration type distributions, update frequencies, change motivations, and maintenance behavior patterns. Contribution/Results: We find that 25.8% of top GitHub users publicly maintain dotfiles; editor and shell configurations dominate (>80%); 63.3% of updates stem from personal customization rather than application dependencies; and update frequencies exhibit substantial inter-user variability—reflecting highly individualized configuration practices. These findings fill a critical gap in empirical software configuration research and provide data-driven insights to inform the design of configuration management tools and infrastructure.

This work addresses the limited cross-file contextual awareness of code large language models (CodeLLMs) in repository-level code generation. Methodologically, we introduce RepoExec—the first executable and functionally correct repository-level benchmark—and propose Dependency Invocation Rate (DIR), a novel metric quantifying the accuracy of cross-file dependency invocation. We further design an instruction-tuning dataset integrating test-driven validation and context-aware dependency modeling. Our contributions include the first comprehensive evaluation framework encompassing context-awareness, execution-driven assessment, and cross-file dependency modeling. Experimental results demonstrate that instruction tuning significantly improves contextual utilization and debugging capability, whereas pre-trained models exhibit stronger functional correctness. RepoExec has since become the de facto standard benchmark for repository-level code generation research.

This work addresses the lack of a unified configuration governance mechanism in heterogeneous multi-agent systems, which hinders versioned, auditable, and cross-framework consistent management. The authors propose a framework-agnostic reference model for agent configuration governance that normalizes diverse configurations into a canonical configuration graph via semantic projection and enforces uniform governance semantics over this graph. Key innovations include typed and independently versioned configuration items, strict decoupling of configuration from runtime, a lattice-based monotonic influence propagation mechanism, and dependency-aware immutable revisions with provenance tracking. The model is validated across LangGraph, CrewAI, and OpenAI Agents SDK, demonstrating governance-equivalent ACM representations across 27 governance scenarios and 9 propagation cases, while guaranteeing convergence, termination, and a unique fixed point.

This work addresses the limitations in training and evaluating coding agents, which stem from a scarcity of executable, verifiable tasks that reflect real-world software environments. The authors propose a novel paradigm that automatically transforms merged pull requests from code repositories into coding tasks—such as bug fixes and feature additions—that can be executed against modern codebases. By leveraging Patch Reversal, Code Mapping, and Agent Reconstruction techniques, the approach aligns historical code changes with current repository states, enabling end-to-end validation of task lifecycles. Evaluated on 1,130 source changes, the method achieves a 79.6% task construction success rate, recovering 29.2% more tasks than baseline approaches, with 98.0% result consistency and a 10.8% reduction in pipeline overhead.