engineering practices

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.

Japan faces the “2025 cliff”—a critical inflection point where aging core IT systems are approaching end-of-support, triggering surging maintenance costs, severe system opacity (“black-boxing”), and impediments to digital transformation (DX). Method: This study proposes a scalable, industrial-grade DevOps solution built on a GitHub–Jenkins–AWS–Docker stack, featuring an end-to-end CI/CD pipeline that dynamically provisions and decommissions isolated, containerized development environments. The approach enables on-demand elastic scaling and secure, sandboxed technology experimentation. Contribution/Results: Deployed at enterprises including Asahi Group, the solution reduces reliance on manual maintenance and mitigates service-outage risks. It shortens middleware and OS upgrade cycles by 60% and cuts annual maintenance costs by ~40%. The work establishes a reusable, highly adaptable DevOps paradigm for legacy system modernization—demonstrating both practical engineering viability and broad academic transferability.

In modern CI/CD pipelines, manual intervention in unstable test diagnosis, rollback decisions, feature flag tuning, and canary promotion introduces release delays and operational overhead. To address this, we propose an AI-augmented autonomous software delivery framework that integrates large language models (LLMs) with policy-constrained autonomous agents, yielding a reference architecture for agent-based decision-making bounded by formal policies. Our approach introduces: (1) a taxonomy of deployment decisions; (2) policy-as-code guardrails enforcing safety and compliance; (3) a tiered trust framework governing agent autonomy; and (4) a DORA-metrics-driven, verifiable evaluation methodology. Evaluated in a React 19 microservices environment, the framework significantly reduces deployment latency and manual intervention frequency, improves release velocity and system reliability, and ensures auditable, formally verifiable autonomous decision paths.

This study presents the first empirical investigation into the evolution of CI/CD configurations in machine learning (ML) projects. Addressing the lack of understanding regarding how CI/CD configurations co-evolve with ML components, the authors analyze 508 open-source ML projects, 343 manually annotated commits, and 15,634 automated CI/CD commits. They propose a novel 14-category taxonomy capturing synergistic changes between CI/CD and ML components, develop a dedicated clustering tool to identify recurrent evolutionary patterns, and establish an empirically grounded model linking developer experience to CI/CD configuration modification behavior. Results show that 61.8% of CI/CD-related commits involve build strategy modifications; common anti-patterns—including dependency hardcoding and missing test frameworks—are identified; and senior developers modify CI/CD configurations more frequently and effectively than juniors, confirming the critical role of experience in CI/CD maintenance.

Continuous Integration (CI) practices suffer from severe monitoring deficiencies: developers largely neglect critical metrics such as “build health” and “time-to-fix failed builds,” while mainstream CI services offer only weak native monitoring capabilities, forcing reliance on fragmented and often redundant third-party tools. Method: We conducted a triangulated investigation—including documentation analysis, developer surveys, functional audits of CI platforms, and case studies of open-source projects—to systematically identify cognitive gaps and practical monitoring needs. Contribution/Results: Our study provides the first empirical evidence that although over 80% of developers track test coverage, only a minority monitor build health or timeliness; further, all major CI services lack built-in multidimensional monitoring support. These findings establish an evidence-based foundation for designing next-generation CI monitoring frameworks and prioritizing tooling enhancements.

This work addresses the fragility, inefficiency, and strong platform coupling commonly found in CI/CD pipelines for legacy COBOL systems, which often result in high maintenance costs and vendor lock-in. To overcome these challenges, the authors propose a portable CI/CD architecture tailored for highly secure and compliance-driven environments. The approach leverages OCI-compliant container images preloaded with COBOL toolchains, introduces a platform abstraction layer, integrates multiple repositories, and employs Groovy script refactoring to achieve platform-agnostic continuous integration and delivery. Empirical evaluation demonstrates that the proposed solution significantly enhances efficiency—reducing pipeline execution time by 82%—while simultaneously improving system portability, security, and maintainability. This architecture offers a reusable paradigm for modernizing legacy COBOL applications within regulated domains.

This study addresses the lack of systematic understanding regarding the evolution of GitHub Actions workflows. Through a mixed-methods approach, we conduct the first large-scale empirical analysis of over 3.4 million workflow file versions from more than 49,000 repositories spanning November 2019 to August 2025. We identify seven categories of conceptual changes and find that repositories typically contain a median of three workflow files, with 7.3% of workflows modified weekly—approximately 75% of which involve only a single change, predominantly in task configuration and specification. Our findings further indicate that current large language model (LLM) tools have not yet significantly influenced workflow maintenance frequency, offering empirical grounding for the design of fine-grained automated maintenance tools.

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.

This study addresses the long-standing lack of effective evaluation of sustainability in scientific open-source software. It proposes a data-driven approach to systematically uncover, for the first time, the relationships among code quality, test coverage, and software sustainability. By analyzing code structure, measuring test coverage, and modeling code–test correlations, the authors classify and compare projects within the CASS software portfolio. The findings reveal that sustainable projects consistently exhibit higher and more stable test coverage, along with clearer mappings between code and tests. In contrast, scientific software as a whole demonstrates generally low test coverage, and high code complexity coupled with strong coupling significantly impairs its testability.