software design and architecture

Software architecture suffers from ambiguous abstraction concepts and inadequate tool support. Method: This work systematically reconstructs the seminal 1995 architectural model and proposes, for the first time, a practice-grounded conceptual framework for architectural abstraction—elevating component composition relationships to system-level abstractions that are formally modelable and verifiable. It integrates architectural description language (ADL) design, abstract modeling, prototype tool development, and diachronic historical analysis. Contribution/Results: The study establishes software architecture as an independent concern with rigorous theoretical foundations. Its outcomes catalyzed a surge in ADL research, laid the groundwork for model-based systems engineering (MBSE), and continue to inform the design of cloud-native, microservice, and AI-driven architectures. The framework significantly enhances the expressiveness, formal verifiability, and engineering applicability of architectural abstractions.

In software design, paradigm-implied semantic expectations—such as data abstraction consistency and feedback-control closed-loop behavior—are often left implicit, leading to design deviations and verification challenges. To address this, we introduce the concept of *design obligations*: explicit, logically formalizable, and verifiable specifications that codify such implicit constraints inherent to design paradigms. Leveraging formal modeling and paradigm semantics analysis, we establish two obligation frameworks—one for data-abstraction-based systems and another for feedback-driven adaptive systems—precisely capturing their core semantic requirements. We demonstrate that common design flaws stem from obligation violations and show how these obligations enable rigorous compliance verification and pedagogical application. This work bridges the semantic gap between design intent and implementation, providing both theoretical foundations and a methodological framework for paradigm-driven design assurance.

This study addresses the lack of systematic, large-scale analyses of structural properties in software feature models, which has hindered the understanding and evolution of variability models. For the first time, it systematically applies large-scale network analysis to 5,709 variability models drawn from 20 repositories. By constructing graphs capturing transitive dependencies and conflicts among features, and integrating graph modeling with network-theoretic and statistical analyses, the work uncovers cross-domain structural commonalities—such as dependency dominance, high centralization, and characteristic degree distributions—as well as domain-specific deviations. These findings provide novel empirical insights and a foundation for identifying pivotal features, guiding modular decomposition, and assessing structural fragility in variability-intensive systems.

Contract-based design (CbD) lacks systematic empirical evidence supporting its application in trustworthy software systems. Method: This study conducts the first systematic mapping study (SMS) of CbD across the full lifecycle and multiple dimensions for trustworthy systems, employing tri-database collaborative retrieval, collaborative review, and voting-based screening to analyze 288 primary studies via thematic coding and evidence aggregation. Contribution/Results: The study clarifies the breadth and depth of CbD adoption across domains, quantitatively assesses domain maturity distributions, and identifies critical gaps among theoretical modeling, automated verification, and industrial practice. It proposes six empirically grounded, verifiable research directions and delivers the first evidence-based CbD methodology framework and practical guidelines for trustworthy software engineering.

This study addresses the lack of systematic guidance for enterprise software teams in choosing between monolithic and microservices architectures. The work proposes a decision-making framework that integrates technical and organizational factors, evaluating the trade-offs of each architecture across dimensions such as scalability, reliability, deployment efficiency, and organizational complexity. The assessment is grounded in system scale, business requirements, operational maturity, and long-term maintainability. Through architectural pattern analysis, a structured evaluation model, and multiple case studies, the authors develop a practical selection methodology tailored to real-world engineering contexts. This approach offers enterprises clear architectural evolution pathways and actionable guidelines aligned with their developmental stages, thereby significantly enhancing the rationality and sustainability of system design decisions.

This study addresses the challenge in software maintenance of effectively quantifying the execution status of internal modules to identify redundant or critical components requiring modification or removal. To this end, it introduces spatial statistics theory into software engineering for the first time, proposing the concept of “software space.” By modeling execution data through a module call graph, the approach enables structured analysis of module-level execution behavior via spatial clustering visualization and statistical hypothesis testing. Experimental results demonstrate that the method successfully identifies both critical and redundant modules, thereby offering data-driven support for informed maintenance decisions.

This work addresses the challenge that ROS 2 systems often embed their layered architecture implicitly within launch configurations, lacking an explicit, standalone architectural view and thereby hindering maintainability. To overcome this, the paper introduces the first approach that models the layered structure as a first-class architectural view in ROS 2. It proposes an automated recovery method combining deterministic parsing with a large language model (LLM) agent, where UML modeling and structural contracts constrain the LLM’s synthesis process, and architectural blueprints guide verifiable, high-fidelity reconstruction. Evaluation on three ROS 2 repositories—including an industrial-scale subset—demonstrates high precision across all abstraction levels, though recall for subsystems declines in complex systems due to the implicit semantics of launch configurations.

This study addresses the challenge of quantifying the complexity and cost induced by external requirement changes when detailed knowledge of a system’s internal logic is unavailable. To this end, the authors propose a black-box assessment method based on a directed graph of component coupling. By analyzing component interfaces and integrating multi-view modeling—graphical, algebraic, and tabular—the approach uniquely links interface characteristics to cost factors, enabling computable bounded estimates of change-induced complexity and associated costs. The method was validated through a large-scale integration case in a retail banking platform, demonstrating its effectiveness and providing architects and operations teams with actionable, quantitative insights for system design and maintenance.

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.