framework selection

This work addresses the lack of a scalable, traceable, and systematic approach to modernizing large-scale legacy systems while preserving both functional and non-functional characteristics. The authors propose a four-phase model-driven method that leverages a semantically rich intermediate model to uniformly abstract a legacy system’s structure, dependencies, and metadata. By designing semantics-preserving transformation rules, the approach enables semi-automated migration to modern platforms such as web-based architectures. The method establishes an end-to-end model-driven pipeline that integrates semantic metadata modeling with automated code synthesis. Evaluated on an industrial-scale .NET system, it successfully migrated core UI components, significantly enhancing maintainability and scalability while reducing modernization risks and manual effort.

This study addresses the proliferation of functional redundancy in service-oriented architectures caused by heterogeneous clients, which undermines system evolvability and maintainability. To mitigate this issue, the authors propose a novel reference architecture that synergistically integrates metadata-driven mechanisms with pattern languages. By leveraging metadata management and a plugin-based design, the approach effectively constrains service redundancy while enhancing reuse capabilities. The work innovatively combines metadata mechanisms and pattern languages in architectural construction and validates its efficacy through a triangulated evaluation method incorporating scenario-based assessment and real-world case studies. Empirical results demonstrate that the majority of system changes during evolution require no code modifications—only configuration adjustments or the addition of pluggable components—thereby significantly improving architectural stability and reuse efficiency.

Current tool-augmented large language model (LLM) ecosystems suffer from fragmentation—characterized by coexisting heterogeneous protocols (e.g., OpenAI Function Calling, Toolformer), manual schema definition, and complex execution orchestration—leading to low development efficiency and high integration overhead. To address this, we propose a protocol-agnostic unified tool integration framework. Our approach introduces an abstract protocol layer for cross-standard compatibility, an automated schema inference mechanism to eliminate manual specification, and a dual-mode concurrent scheduler enabling seamless synchronous and asynchronous tool execution. Experimental evaluation demonstrates that, compared to baseline approaches, our framework reduces implementation code volume by 60–80%, achieves up to 3.1× improvement in end-to-end execution latency, and maintains full backward compatibility with mainstream LLM tool-calling ecosystems.

This study addresses the absence of benchmarks for evaluating behavior-preserving migrations of enterprise Java applications across frameworks such as Spring, Jakarta EE, and Quarkus. The authors introduce the first systematic benchmark comprising 34 applications, 102 variants, and 204 directed migration tasks, along with an end-to-end correctness validation mechanism based on executable oracles that ensures functional equivalence through compilation, containerized deployment, and interface behavior testing. Experimental results reveal that even the most advanced coding agents achieve only a 15.3% pass rate for single-layer migrations and 12.2% for full-application migrations, with merely one of the 204 tasks attaining complete behavioral equivalence. The study further uncovers significant asymmetries in migration difficulty across framework directions and architectural layers and identifies recurring failure patterns spanning build, deployment, and testing phases.

Current RESTful API design quality assessment relies heavily on manual inspection, lacking early, automated validation mechanisms for non-functional requirements—particularly interoperability, modularity, and maintainability. Method: This paper proposes an OpenAPI-based static analysis approach that implements a configurable rule engine. It formalizes 75 design principles derived from scholarly literature and industry standards into structured, machine-checkable constraints, enabling customizable rule activation/deactivation and traceable feedback to align requirements engineering with architectural governance. Contribution/Results: Following the design science research paradigm, we developed and evaluated a prototype tool. Empirical evaluation and expert review demonstrate that the method significantly improves API design compliance and consistency, achieving 82% automation coverage. It effectively supports continuous architectural governance in agile development environments, bridging the gap between design-time assurance and operational API lifecycle management.

This study addresses critical pain points in AUTOSAR Adaptive application development by revealing, for the first time from an industrial practice perspective, their root causes: inherent challenges arising from the interplay among the specification’s own architectural design and reuse objectives, vendor-specific implementation variations, and localized usage patterns. Employing a design science research methodology, the authors construct a minimal viable platform prototype and integrate configuration management analysis with runtime lifecycle modeling to systematically identify and attribute key issues. The primary contribution lies in demonstrating that design flaws at the specification level are the core catalysts of these challenges. The work further proposes optimizing the toolchain to reduce configuration complexity and training overhead, thereby offering empirical evidence and actionable pathways for improving the AUTOSAR Adaptive ecosystem.

Software maintenance remains heavily reliant on manual effort, resulting in high costs, low efficiency, and susceptibility to errors. This work proposes the first systematic research framework for transfer-based software maintenance, drawing inspiration from transfer learning. The framework establishes a comprehensive lifecycle model encompassing task identification, source system selection, cross-system data matching and adaptation, and validation of transferred outcomes. It explicitly delineates the core objectives and key challenges at each stage, integrating techniques from software engineering such as knowledge transfer, cross-project data alignment, and context-aware adaptation. By doing so, the framework introduces a novel paradigm for automating software maintenance and lays a solid theoretical foundation for the future development of supporting tools and methodologies.

本文提出了一种基于仓库的实现方法，通过自动接口更新和一致性检查减少有人和无人飞行器软件开发中跨域不一致问题。

This study addresses the limited understanding of how migration guides are actually provided and utilized by developers, a gap that undermines their effectiveness in managing breaking changes in software libraries. Focusing on real-world usage practices, the work presents an empirical investigation centered on libraries with incompatible updates—such as Log4j—by analyzing pull request data and patterns of documentation referencing. The findings reveal that 82.81% of references point to entire migration guides rather than specific sections, and that these guides serve not only during major version upgrades but also play a sustained role in long-term maintenance. These insights offer empirically grounded recommendations for improving the design and utility of API migration documentation.

This study addresses the limitation of existing templates in specification-driven development, which fail to evaluate specification clarity and completeness. To overcome this, we propose EPIC, a framework grounded in the ISO/IEC/IEEE 29148 standard that conducts quantitative assessments of open-source repositories. By distilling an optimal specification taxonomy encompassing ten quality dimensions and forty practices, EPIC guides developers in clarifying expectations and bridging specification gaps. Empirical evaluations demonstrate that high-quality specifications reduce the proportion of bug-fixing commits to 11.8% and yield a fourfold increase in the median number of contributors. These findings indicate that adopting rigorous specification practices significantly enhances both collaborative efficiency and software quality in open-source projects.