full-stack development

Current micro-frontend architectures heavily rely on specific bundlers (e.g., Webpack), leading to inflexible module composition, constrained cross-team collaboration, and bottlenecks in error detection, runtime observability, and loading performance. To address these limitations, we propose Bundler-Independent Module Federation (BIMF)—the first runtime module federation framework decoupled from build-time bundlers. BIMF enables dynamic module loading, type-safe inter-module collaboration, and cross-team dependency sharing. It integrates runtime dependency resolution, distributed tracing, server-side rendering (SSR), and intelligent prefetching to significantly enhance observability and first-contentful-paint (FCP) performance. Experimental evaluation of a prototype implementation demonstrates: (1) full preservation of TypeScript type contracts across modules; (2) 100% dependency deduplication; (3) a 37% reduction in average module loading latency; and (4) a 42% improvement in parallel development efficiency across distributed teams.

This work addresses the challenges in edge and embedded application development—namely, heterogeneous software stacks, multi-language runtimes, and difficult debugging—which lead to rigid deployment workflows and complex fault diagnosis. To overcome these limitations, the paper proposes a novel architecture enabling unified end-edge-cloud development. Its core components include a single programming language, a retargetable runtime system, a local recording and replay mechanism for distributed events, and a cross-platform deployment framework. This design breaks down traditional debugging barriers in edge–cloud collaborative development, facilitating seamless scalability, consistent testing, and flexible deployment across heterogeneous environments. Evaluation of the prototype system demonstrates that the proposed approach significantly simplifies deployment procedures and enhances fault diagnosis efficiency.

To address inefficient business modeling, overreliance on CRUD paradigms, and neglect of underlying computational principles in full-stack development, this paper proposes a graph-theoretic development methodology: business logic is uniformly modeled as directed acyclic graphs (DAGs), and six novel graph-traversal-based development paradigms—including Program-Behavior Flow Diagram (PBFD)—are introduced. Innovatively, bitmaps replace relational join tables, enabling co-optimization of storage structure, query performance, and data consistency. Validated over eight years in industrial settings, the approach achieves zero-defect delivery, 20× improvement in development productivity, 7–8× speedup in runtime performance, and reduces storage overhead to 1/11 of conventional solutions. The core contribution lies in deeply integrating foundational graph theory into full-stack engineering practice, establishing a formally verifiable, scalable, and high-performance software construction paradigm.

This study addresses the practical disparities and co-evolution between high-performance computing (HPC) and edge computing architectures within the cloud continuum. It presents the first large-scale empirical analysis based on 396 real-world, production-grade AWS architectures. Methodologically, we propose a multidimensional, data-driven framework encompassing service topology identification, storage type classification, architectural complexity quantification, and ML service integration statistics. Results reveal systematic differences—and complementary patterns—between HPC and edge architectures across four dimensions: core service composition (e.g., EC2 versus Greengrass/Lambda), storage design paradigms (parallel file systems versus distributed lightweight caches), complexity distributions, and ML embedding strategies. This work delivers the first industry-scale architectural benchmark for the cloud continuum, providing empirically grounded insights and methodological foundations for cross-domain architecture design, resource optimization, and cloud-native convergence of HPC and edge computing.

This study investigates the similarities and differences between DevOps specialists and general software developers with respect to tool usage, technical preferences, career stage, and work arrangements, leveraging the Stack Overflow 2023 Developer Survey dataset. Employing Python (Pandas) for large-scale data cleaning and statistical analysis, the research reveals substantial overlap in the adoption of critical tools such as Docker and Kubernetes. However, DevOps practitioners are predominantly mid-career professionals, whereas general developers tend to be younger. Both groups exhibit widespread adoption of remote and hybrid work models. By providing empirical insights into the evolving roles and collaborative dynamics between these two developer archetypes, this work addresses a notable gap in the literature and underscores their complementary relationship and growing synergy within modern software development ecosystems.

This work addresses key challenges in migrating existing REST APIs to GraphQL—namely, type unsafety, high performance overhead, and tight coupling to specific data sources. The authors propose an automated migration approach based on static program analysis that accurately infers types to construct a type-safe GraphQL schema. By embedding a GraphQL server that directly reuses the original API logic, the method eliminates network calls and adapter-layer overhead. This approach achieves end-to-end type safety for the first time, demonstrating 100% conversion success and zero type errors across 834 REST endpoints from nine open-source projects. Compared to OASGraph, it substantially improves both correctness and performance, accelerating data retrieval by 2–4×.

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 work addresses the challenge of maintaining up-to-date architectural documentation in microservice systems, which is exacerbated by polyglot implementations, multiple repositories, and rapid independent evolution. Existing static refactoring approaches are often limited to single-repository settings or homogeneous technology stacks. To overcome these limitations, we propose a distributed static architecture reconstruction framework that supports multi-language and multi-repository environments. The framework employs pluggable extractor modules for language-specific analysis and introduces mechanisms for cross-repository data propagation and fusion, enabling seamless interoperability with existing static analysis tools. To the best of our knowledge, this is the first framework to enable distributed, collaborative architecture reconstruction, significantly enhancing the scalability and usability of automated documentation generation and maintenance in complex microservice ecosystems.

This study addresses the lack of systematic understanding regarding how GitHub Actions workflows are used in real-world scenarios, how developers respond to workflow failures, and how these practices relate to project characteristics. Combining large-scale quantitative analysis of 258,300 workflow runs with qualitative case studies across 21 diverse repositories, this work identifies three typical patterns developers employ to handle workflow failures and uncovers a “configuration–usage gap”—where YAML configurations exist but workflows remain effectively unused. Furthermore, the study empirically validates five hypotheses linking project features to workflow usage intensity, revealing a significant positive correlation between high usage intensity and low failure rates. These findings provide actionable empirical evidence for improving CI/CD practices.