interface prototyping

This work addresses the inefficiency and lack of guidance faced by front-end developers when manually selecting plausible and natural attribute values for instantiating reusable UI components within a vast design space. To tackle this challenge, the paper introduces the concept of “discriminative variants,” which uniquely integrates symbolic reasoning with large language models (LLMs). Symbolic reasoning identifies visually salient attributes, while the LLM leverages real-world knowledge to generate component instances that balance fidelity to exemplars with meaningful differentiation. This approach shifts the paradigm from ad hoc manual configuration to structured exploration of the design space. A user study (n=12) demonstrates that the generated variants effectively aid developers in comprehending the design space, significantly improving both instantiation efficiency and user experience, while maintaining strong domain relevance.

Interactive AI systems suffer from architectural opacity, poor interpretability, and limited controllability due to model black-box nature. Method: This paper proposes a transparency-oriented architecture based on composable, structured modules—including AI models and control logic—introducing a novel “structure + visualization” dual-track modular design. It integrates both post-hoc and inherently interpretable XAI techniques, defines an LLM-coordinated explanation protocol, and implements a visual explanation interface with explicit dataflow and API specifications. Contribution/Results: The work establishes the first end-to-end interactive modeling framework enabling human-AI co-understanding. A prototype system empirically validates consistent behavioral understanding across developers, end users, and LLMs, significantly improving system controllability and debugging efficiency.

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.

Non-AI engineers lack efficient tools to prototype generative AI-based UI Agent experiences. Method: We propose AgentBuilder—a user-centered design framework and lightweight prototyping tool—developed through contextual inquiry, design probes, and in-situ experiments to identify core activities and capability requirements for agent experience design. AgentBuilder supports low-code interaction orchestration, real-time LLM integration, and iterative multi-turn dialogue prototyping. Evaluation with 14 cross-disciplinary participants demonstrated that the framework significantly lowers prototyping barriers, enhances designer engagement, and improves feedback quality. It further revealed non-technical users’ critical needs for controllability, explainability, and progressive guidance. Contribution/Results: This work presents the first systematic methodology and practical toolchain for UI Agent experience prototyping tailored specifically for non-engineers, bridging a critical gap between human-centered design and generative AI interface development.

Existing UI prototyping tools provide weak support for integrating design artifacts such as screenshots and sketches, hindering component reuse, semantic integration, and cross-role collaboration. This paper proposes a novel UI prototyping paradigm grounded in Conceptual Blending Theory, the first to concretize cognitive-science-based blending mechanisms into an interactive tool. It enables semantic-level element mixing across heterogeneous design examples through example-driven component extraction and semantic alignment, lightweight vision–semantics mapping, and real-time blended preview—facilitating staged intent articulation by developers. An empirical study with 14 frontend developers demonstrates that the approach significantly reduces prototype initiation time (average improvement of 42%), stimulates highly unexpected creative combinations (68% novel composition rate), and enhances design–development collaboration efficiency.

This work addresses the lack of explicit modeling of abstraction mechanisms in existing interactive system design, which hinders actionable design guidance. Through a systematic review of 457 publications, the study proposes the first abstraction-centered design space for interactive systems, structured around six core dimensions. Leveraging this framework, it reconceptualizes the Gulf of Execution and Evaluation model to reveal the cognitive and design mechanisms by which users and systems bridge the abstraction gap. By explicitly integrating abstraction into the theoretical foundations of human–computer interaction, this research synthesizes prior work, establishes a coherent theoretical basis, and offers systematic practical guidance for designing and evaluating abstraction mechanisms in interactive systems, thereby charting new directions for future inquiry.

This work addresses key challenges in professional software automation, including the fragility of GUI-agent visual grounding, error propagation, and the limitations of API-based approaches due to protocol heterogeneity and closed interfaces. To overcome these issues, the paper introduces COM-as-Action, a novel paradigm that leverages the Component Object Model (COM) as a unified executable abstraction, reframing software interaction as deterministic program synthesis. The contributions include ComCADBench—the first industrial-scale CAD benchmark—ComActor, a self-correcting agent designed for robust task execution, and ComForge, an extensible platform enabling large-scale training. Experimental results demonstrate that ComActor achieves state-of-the-art performance on ComCADBench, significantly outperforms baseline methods on long-horizon tasks, and successfully generalizes to external CAD environments.

This work addresses the time-intensive nature of GUI prototyping and its reliance on specialized expertise, particularly the limited support existing tools offer for efficiently evolving non-code artifacts during iterative requirement changes. The authors propose an automated approach leveraging Retrieval-Augmented Generation (RAG) and human-AI collaboration: it first parses user-provided natural language inputs—supporting Chinese, English, and Thai—to extract GUI design features, then retrieves relevant components from a custom library of 32 base elements to generate editable Figma prototypes with automatic layout. Built on the Gemini 2.5 Flash large language model and a new Node.js/Express architecture, the system incorporates a user-adjustable feature refinement step prior to prototype generation. Preliminary evaluation shows that users produce more prototypes within a fixed timeframe, and expert assessments across nine quality dimensions significantly favor the generated outputs over manually crafted ones.

This work proposes a new paradigm for software design tailored to AI agents as primary users, addressing the limitations of traditional human-centric approaches. It formally defines the concept of an “agent interface” for the first time, centering on callable capabilities and emphasizing machine interpretability, composability, and invocation reliability. Guided by the interaction requirements of large language model–based agents, the study introduces a capability-oriented software architecture and corresponding interface specifications. By establishing a conceptual foundation and design framework for AI-native systems, this research advances software engineering beyond monolithic applications toward dynamic, composable ecosystems of interoperable capabilities.

This work proposes a novel software construction paradigm that fundamentally departs from the core assumptions of object-oriented programming. By decoupling problem-domain entities from functional modules, the approach enables independent organization and reuse of functionality, thereby addressing the fragmentation of features and entanglement of responsibilities commonly induced by binding system behavior to individual domain objects. The proposed paradigm adopts a non-object-oriented modular design that substantially mitigates architectural coupling stemming from rigid object boundaries. As a result, it offers a clearer, more flexible, and maintainable pathway for modeling complex systems, overcoming key limitations inherent in traditional object-oriented approaches.