In the evolving landscape of data architectures, the Data Mesh paradigm has emerged as a scalable and decentralized approach to managing complex data ecosystems. However, as data domains grow increasingly autonomous, ensuring consistency, discoverability, and transformation standardization across domains becomes challenging. This paper proposes a metadata-driven approach to data transformation within Data Mesh architectures. By leveraging active metadata such as data lineage, transformation rules, quality metrics, and semantic definitions data domains can automate and orchestrate transformations in a consistent, governed, and scalable manner. The paper explores architectural patterns, implementation considerations, and case studies demonstrating the benefits of this approach in enhancing interoperability, governance, and agility in enterprise data systems.
Lei Weing, Wei Chen· International Journal of Art...· 0 citations
Modern software development increasingly integrates neural models such as code completion engines, automated refactoring systems, and learned optimization modules into traditional codebases, creating hybrid environments commonly referred to as neural-augmented codebases. While these systems achieve significant productivity gains, debugging them presents unique challenges due to the opaque reasoning processes of neural components. Conventional fault localization techniques are insufficient, as they cannot effectively attribute errors originating from model-generated code, neural decision boundaries, or interactions between learned and symbolic components. This paper proposes an explainable fault localization framework that combines program analysis, runtime tracing, and interpretable machine learning techniques to identify, rank, and justify the root causes of failures in neural-augmented codebases. By integrating explainability mechanisms such as attention heatmaps, causal dependency graphs, and interpretable embeddings, the framework enhances developer trust, reduces debugging cost, and provides actionable diagnostic insights. Experimental evaluation on real-world hybrid systems demonstrates improved fault detection accuracy, reduced false positives, and higher interpretability scores compared to state-of-the-art approaches. The results show that explainability is not merely an auxiliary feature, but a critical enabler of scalable, safe adoption of neural components in modern software engineering workflows.
Lei Weing· International Journal of App...· 0 citations
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