production systems

To address the challenges of standardizing Site Reliability Engineering (SRE) practices in heterogeneous environments and balancing system reliability with development agility, this paper proposes a customizable SRE process framework. The framework integrates automated operations, multidimensional observability (metrics, logs, traces), error-budget-driven governance, standardized incident response, and progressive delivery (canary and blue-green deployments). It is designed for cross-technology-stack adaptability, enabling contextual implementation of core SRE principles. Evaluated in production systems, the framework reduced mean time to recovery by 42%, decreased unplanned outages by 67%, lowered operational staffing requirements by 35%, and achieved 99.99% service availability. Its primary contribution is the first methodology for customizing SRE processes specifically for heterogeneous environments, empirically demonstrating synergistic improvements in both system reliability and operational efficiency.

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.

In large-scale continuous software engineering (CSE), cross-team, cross-functional, and external dependencies frequently induce coordination delays and delivery bottlenecks. Method: Through 17 semi-structured interviews and thematic coding analysis, this study systematically identifies— for the first time—the three critical dependency types between software teams and supporting departments or external entities, along with their underlying bottleneck mechanisms. Contribution/Results: We propose the first dependency classification and analysis framework specifically designed for large-scale CSE. Empirical findings demonstrate that cross-functional and external dependencies are the primary drivers of performance degradation. The framework establishes a theoretical foundation for understanding complex interdependencies in CSE and explicitly prioritizes resilience-oriented coordination mechanisms. It thereby addresses a critical gap in empirical research on dependency management within large-scale CSE contexts.

In complex organizations, product diversity, legacy systems, organizational inertia, and regulatory constraints severely impede the adoption of end-to-end Continuous Software Engineering (CSE). Method: Drawing on empirical studies across automation, automotive, retail, and chemical industries, this paper proposes an evolutionary CSE adoption pathway. It extends the CSE readiness model by introducing explicit internal and external feedback layers and distinguishing market constraints (e.g., compliance requirements) from organizational constraints (e.g., process rigidity), thereby enabling phased, context-sensitive implementation. The model is validated and refined through expert interviews and narrative synthesis. Contribution/Results: Results demonstrate that—even without achieving full-chain continuous delivery—prioritizing internal engineering capability enhancement significantly improves delivery efficiency and business responsiveness. The extended readiness model supports pragmatic, incremental CSE adoption in highly regulated, heterogeneous environments.

Modern cyber-physical system (CPS) software must continuously deliver safety-critical functionality, yet existing software factories lack deep integration of safety engineering practices, leading to a disconnect between development and safety assurance. Method: This paper introduces the “Safety Factory” paradigm—a novel approach that systematically integrates formal modeling, automated consistency verification, semantically rich safety models, and CI/CD pipelines. It enables model-based, machine-processable, and continuously evolving safety activities through automated documentation generation, formal verification, and safety-aware build pipelines. Contribution/Results: The framework ensures functional safety while significantly improving safety compliance efficiency. Empirical evaluation demonstrates its capability to support rapid iteration and concurrent safety assurance for complex CPSs, effectively bridging the gap between high-velocity development and stringent safety requirements.

To address interdisciplinary interoperability, variant configuration governance, end-to-end traceability, and cross-organizational collaboration challenges arising from the networked evolution of Systems of Systems (SoS), this paper proposes a lifecycle management framework for Network-Centric Development (NCD). Methodologically, it grounds the framework in Model-Based Systems Engineering (MBSE) semantics and integrates Product Lifecycle Management (PLM) governance, CAD-CAE model synchronization, and closed-loop digital thread/digital twin capabilities. Its core contributions are four foundational principles: (1) reference architecture with a unified data model; (2) end-to-end configuration sovereignty; (3) review-driven model gating; and (4) quantifiable value contribution assessment. Empirical validation across transportation, healthcare, and public-sector domains demonstrates significant improvements in change robustness and model reuse rate, reduced delivery cycles, and enhanced support for sustainability-oriented decision-making.

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 study investigates software development teams’ awareness, attitudes, and readiness for organizational change prior to migrating to Software Product Line (SPL) engineering in Small and Medium-sized Enterprises (SMEs). Using semi-structured, in-depth interviews with key stakeholders across multiple roles, we conducted a qualitative study grounded in an SPL implementation framework and applied thematic analysis. Results indicate unanimous recognition of the migration’s strategic benefits, confirming the critical role of early stakeholder engagement in mitigating transition risks. Based on empirical findings, we propose a three-dimensional strategy to alleviate resistance to change: sustained cross-functional communication, incremental adoption of existing practices, and inclusive, collaborative implementation. This work addresses an empirical gap by systematically assessing pre-migration organizational cognition within SMEs—contextually distinct from large enterprises—and delivers actionable, context-sensitive change management guidance tailored for resource-constrained software organizations undertaking SPL adoption.

This study addresses the limited understanding of how practitioners actually develop software engineering (SE) agents, particularly the lack of systematic investigation into the evolution of development workflows and core challenges. Through semi-structured interviews with 20 practitioners complemented by a survey of 80 respondents, this work proposes the first seven-stage workflow for SE agent development, revealing a paradigm shift toward “evaluation-driven iteration.” The research identifies that bottlenecks have moved beyond coding to non-coding tasks such as requirement specification, cross-role coordination, review, and deployment. It systematically characterizes six key challenges—including unreliable evaluation signals, accumulating comprehension debt, and behavioral drift induced by model updates—and synthesizes corresponding practical mitigation strategies.