backward compatibility engineering

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 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 paper addresses version control challenges for multidimensional structured data—namely, temporal evolution, spatial collaboration, and design iteration—by introducing Operational Differencing, a novel paradigm. Methodologically, it incorporates high-level semantic operations (e.g., schema changes, refactorings) into the version model; adopts an append-only branch history with a repository-free, lightweight “copy-as-branch” architecture; and enables operational query representation and future-tense execution. Contributions include: (1) the first systematic support for automatic schema-adaptive query rewriting under schema evolution; (2) precise, fine-grained diff/merge across structural transformations; (3) resolution of four out of eight canonical schema evolution challenges; and (4) a simplified versioning experience with no explicit repository and asymptotically zero branching overhead.

This study addresses the challenges of high cost, error-proneness, and defect propagation in cross-repository code and test reuse during software refactoring. Through action research, the authors conduct bidirectional empirical analyses on real-world cases such as Soot/SootUp and FindBugs/SpotBugs, identifying for the first time the bidirectional reuse requirements and semantic reuse patterns inherent in refactoring scenarios. They propose a semantic alignment–based code mapping approach coupled with a hierarchical, extensible clone detection mechanism. Experimental results demonstrate that their method reduces irrelevant clones by 33%–99% on average and achieves a benchmark precision of 86%. The practical impact is further evidenced by five reported issues and ten pull requests submitted to open-source communities, eight of which have already been merged, confirming the approach’s effectiveness and applicability.

Legacy systems written in COBOL, PL/I, or Assembly—common in banking and telecommunications—are often undocumented and lack original developers, hindering comprehension and modernization. Method: This paper proposes a multi-language, cross-platform, customizable framework for constructing software knowledge graphs and interactively defining architectural boundaries. It integrates static code analysis, data schema parsing, and custom ontology modeling to enable expert-guided, incremental analysis of source code and data architecture, automatically identifying business- and data-driven logical boundaries and visualizing cross-boundary dependencies. Contribution/Results: The framework introduces the first knowledge-graph-driven approach for progressive modernization path planning and impact analysis. Evaluated on two real-world industrial systems, it significantly improves system understanding efficiency and enhances the accuracy of modernization strategy design.

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 study addresses the limited understanding of relationships between deprecated and replacement APIs across library versions. For the first time, it integrates source code definitions with raw invocation perspectives to investigate 830 deprecation mappings across 33 Python libraries. Through similarity ranking tracking, version-by-version execution testing, and source code analysis, this work systematically examines replacement locality, parameter discrepancies, and lifecycle states. The findings quantitatively reveal complex correlations between dependency granularity and release contexts, alongside distinct replacement distribution patterns. Ultimately, this research provides empirical foundations for evolution-aware API recommendation and automated migration.

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。

为确保MongoDB在多种编程语言中的客户端库行为一致，开发了基于YAML的统一测试格式，减少了非一致性错误。

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.