codify best practices

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.

Rigid activity implementation binding in digital business processes hinders adaptation to heterogeneous organizational requirements. Method: This paper proposes a three-level dynamic binding mechanism—operating at compile time, launch time, and runtime—that enables concurrent execution of multiple implementations for the same activity and supports context-aware, dynamic customization of input/output data contracts. Integrating Software Product Line (SPL) engineering with Process-Aware Information Systems (PAIS), we develop a variability modeling and runtime feature configuration framework. Contribution/Results: Our approach achieves, for the first time, end-to-end flexible activity binding across the full process lifecycle. It overcomes the limitations of conventional single-version, static binding by enabling on-demand composition of diverse activity implementations and data interfaces within a unified process model. This significantly enhances the adaptability and configurability of process systems in multi-organizational settings.

This study addresses the significant burden developers face in authoring and maintaining GitHub Actions workflows, stemming from a lack of systematic understanding of real-world automation and reuse practices. Through a mixed-methods approach combining a survey of 419 practitioners with qualitative and quantitative analysis, this work presents the first developer-centric characterization of common automation tasks, patterns of reuse mechanism adoption, and maintenance pain points in workflow development. The findings reveal that while developers heavily rely on reusable Actions, they seldom adopt reusable workflows; version management challenges lead to rampant copy-pasting; and critical aspects such as security and performance monitoring remain under-automated. These insights provide empirical foundations for improving CI/CD toolchains and reuse mechanisms.

This paper investigates validity threats arising from toolchain selection in quantitative empirical software engineering. We formally replicate three high-impact studies by extracting identical project data using four widely adopted mining tools—Git, JIRA, GitHub API, and BIC—and conduct both quantitative and qualitative comparative analyses. Results demonstrate that subtle technical discrepancies across tools—including data modeling assumptions, event definitions, and temporal window handling—propagate and significantly undermine consistency in baseline datasets, statistical outcomes, and ultimately research conclusions. To our knowledge, this is the first systematic study to reveal the critical impact of tool choice on the robustness of empirical findings. We propose a practical framework comprising enhanced tool reusability, improved analytical transparency, and mandatory cross-tool validation. This work advances methodological rigor in software evolution research by highlighting and mitigating tool-induced validity threats.

This study addresses the longstanding fragmentation in software artifact traceability research, characterized by incomplete linkages, ambiguous techniques, and disconnected application contexts. Through a systematic literature review, it constructs the first comprehensive traceability landscape encompassing 22 artifact types and 23 relationship kinds, and introduces a technology decision map, a standardized evaluation benchmark, and a role-oriented dynamic path alignment framework. The work uncovers critical challenges: a pervasive code-centric bias, a reproducibility crisis stemming from only 37% of studies releasing open-source artifacts, and a significant adoption gap with 95% of proposed tools never deployed in industry. In response, it offers targeted strategies to bridge these gaps, establishing a unified knowledge foundation for future research and practical implementation in traceability.

This study addresses the unclear practical adoption of agentic software engineering methodological frameworks within large-scale code repositories. To investigate this, we conduct the first empirical examination across 116,000 GitHub repositories, integrating mining software repositories techniques, stratified sampling, quantitative artifact detection, and qualitative coding analysis to systematically evaluate the prevalence, co-occurrence, and rule characteristics of eight coordination mechanisms. Our findings reveal an overall presence rate of at least one mechanism at 21.7%, rising to 65.3% among highly starred projects. However, fully integrated systems remain rare, as current practices are predominantly confined to the isolated application of individual mechanisms. This work provides foundational empirical evidence regarding how multi-agent coordination paradigms are currently operationalized in open-source software development, highlighting a significant gap between theoretical agentic frameworks and their holistic real-world implementation.

本文研究了四个去中心化构建包生态系统中的软件制品验证问题，通过定义独立验证模型和实现制品验证管道来解决因元数据缺失、隐式发布转换等问题导致的验证困难。

This study addresses the lack of systematic understanding regarding the application domains, maintenance characteristics, and effective design practices of GitHub template repositories. Conducting the first large-scale empirical investigation, the work integrates data mining, statistical analysis, code quality assessment tools—detecting code smells, vulnerabilities, and security hotspots—and an LLM-as-a-judge classification approach to systematically uncover domain distributions, language-specific quality variations, and maintenance patterns. The findings reveal web development as the dominant application domain, with high-quality templates consistently adhering to software engineering best practices and offering comprehensive documentation. Through qualitative evaluation, the study distills actionable design guidelines and identifies common pitfalls, providing practical guidance for developers creating or using template repositories.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

研究使用基于LLM的流程从敏捷工作项中恢复C4架构图，通过混合方法评估其在两个行业项目中的准确性与实用性。