toolchain interoperability

This study addresses the challenges of maintaining consistency across heterogeneous schema languages—such as JSON Schema, XSD, and SHACL—during multilingual data model evolution, where fragmented converters, variable quality, and information loss impede reliable interoperability. The work proposes a novel approach that models schema languages and black-box converters as nodes and directed edges in a graph, enabling composable and evaluable conversion path orchestration. By integrating graph-based search, quality-aware ranking (combining agent-assisted and human evaluation), and failure backtracking, the method supports automated, reproducible cross-language schema transformation. The resulting open-source toolchain, Schema Conversion Orchestrator, integrated into the MetaConfigurator platform, successfully produced valid outputs for 43 out of 60 real-world tasks and precisely identified missing ecosystem components in the remaining 17, thereby delineating the current boundaries of schema conversion capabilities.

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 work addresses the limitations of current expert-validated “LLM+script” workflows, which lack adaptability, cannot dynamically evolve based on feedback, and offer no effective pathway toward agent-based architectures. To overcome these challenges, the paper proposes a reversible “Strangler Fig” migration framework that transforms static workflows into composable, typed, and auditable stages. It introduces a three-tier convertibility classification—A/B/C—to enable dynamic routing and progressive evolution. This approach uniquely facilitates a smooth, structured transition from legacy LLM workflows to self-evolving agent systems while providing an assessment capability to determine evolutionary readiness.

Ontology interoperability is hindered by conceptual conflicts and semantic overlap, limiting the coordinated use of ontologies in knowledge graphs (KGs). To address this, we propose a full-lifecycle ontology interoperability ecosystem that integrates three complementary semantic technologies: (1) Ontology Design Patterns (ODPs) to support reusable and compatible ontology design; (2) Ontology Matching and Versioning (OM&OV) to ensure evolutionary consistency across ontology updates; and (3) Ontology-Compliant Knowledge Graphs (OCKGs) to enable staged, semantically aligned ontology integration. Evaluated in the construction domain, our approach improves inter-ontology concept mapping accuracy by 32.7% and accelerates KG construction by 41%. It significantly enhances ontology integrability and maintainability in real-world tasks, providing a systematic, methodology-driven foundation for standardized ontology interoperability in domain-specific knowledge graphs.

Existing data pipelines often suffer from weak governance, leading to delayed schema validation, inconsistent cross-language execution, and misalignment with business semantics. This work proposes treating data contracts as types, leveraging the “everything-as-code” paradigm to inject schema annotations—encompassing column types, constraints, documentation, and lineage—into input and output tables within a lakehouse architecture via multi-language SDKs. These annotations are parsed across multiple phases of the execution lifecycle, deeply integrating data contracts into the type system. The approach enables both deterministic and non-deterministic reasoning over data flows across languages and execution engines, significantly enhancing the reliability of production data pipelines and ensuring consistent interoperability across systems.

This work addresses the persistent challenge of inconsistent development and execution environments faced by researchers operating across heterogeneous computing platforms—ranging from laptops and workstations to supercomputers and cloud infrastructures. To overcome this, the authors propose a modular and portable software ecosystem featuring a unified command-line interface that enables seamless orchestration and execution of scientific workflows. The system ensures cross-platform consistency, reproducibility, and scalability, thereby streamlining computational research across diverse hardware configurations. Its practical efficacy has been demonstrated through successful integration into the plan4res project under the European Union’s Horizon 2020 initiative, where it effectively supported complex, large-scale scientific workflows in varied computing environments.

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.

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.