rapid prototyping

To address performance overhead escalation and transaction boundary degradation arising from process decomposition during monolith-to-microservices migration, this paper proposes a lightweight, trace-based what-if analysis method. The approach comprises three stages: execution trace collection and rewriting, performance-sensitive call-chain simulation, and abstract modeling of transaction boundaries—enabling rapid, quantitative assessment of non-functional property changes induced by service decomposition alternatives. Its core innovation lies in introducing the first trace-rewriting analysis paradigm prioritizing usability and speed, requiring neither source-code modification nor deployment in production-like environments. Evaluated on industrial case studies, the method completes each scenario assessment in seconds—achieving two orders-of-magnitude improvement in analysis efficiency—and thereby significantly facilitates high-frequency, low-friction iteration over service boundaries and informed trade-off decisions.

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 protracted development cycles in traditional visual analytics (VA) prototyping that hinder rapid validation of novel ideas. The authors propose a scaffolded, AI-assisted development paradigm centered on the Artifact–Transform Workflow Language (ATWL) as a structured framework, integrating large language model–driven AI assistants with targeted expert interventions to efficiently construct high-quality VA prototypes within hours. The approach successfully instantiated innovative visual designs such as “soft Pareto fronts” and “constellation” groupings. Controlled experiments further revealed the critical influence of scaffolding design, timing of human-AI collaboration, and methods of knowledge injection on prototype quality, leading the authors to advocate for a taxonomy of knowledge expression in human-AI collaborative systems.

Scientific software selection frequently suffers from non-reproducible benchmarks due to multi-library, multi-metric evaluation and dynamic evolution—such as the introduction of new algorithms or modifications to test cases and evaluation criteria. This paper addresses numerical integration over arbitrary 2D/3D domains with implicit or parameterized boundaries (cut-cell quadrature), proposing the first automated benchmarking framework that systematically integrates CI/CD engineering practices into scientific computing workflows. The framework unifies GitHub Actions, Docker, Python-based scheduling, Jupyter-based report generation, and semantically versioned result archiving. It supports automated configuration, execution, visualization, and historical result comparison. It achieves >90% automation for benchmark tasks and regression detection; reduces integration time for new libraries or algorithms by 70%; and enables precise attribution of performance deviations to specific code commits. The framework significantly enhances reliability, reproducibility, and evolutionary adaptability in scientific software evaluation.

This study addresses the challenge faced by resource-constrained software startups lacking user experience (UX) expertise in efficiently developing user-centered minimum viable product (MVP) prototypes. To bridge this gap, the authors propose StartFlow, a lightweight method that uniquely integrates wireframes and user flows into a unified “wireflow” representation. StartFlow guides non-UX teams through a structured three-step process—feature organization, prototype construction, and closed-loop validation based on usability heuristics—to iteratively refine MVPs. Empirical results demonstrate that teams employing StartFlow produce prototypes that are clearer, better aligned with user stories and business rules, and exhibit significantly fewer usability flaws. Expert evaluations further confirm the method’s high usability and strong potential for broad adoption in early-stage software development contexts.

This work addresses the challenge that rapid software development often compromises code maintainability, thereby hindering safe AI-assisted refactoring. To mitigate this, the authors propose an iterative refactoring approach that integrates large language models with human oversight. The method first leverages a code-specialized large language model to automatically generate high-coverage unit tests that capture existing program behavior. Subsequently, developers guide test-driven refactoring, while branch coverage metrics are used to constrain and validate model-generated outputs. Empirical evaluation demonstrates that the approach produces nearly 16,000 lines of reliable test code within hours, achieving up to 78% branch coverage on critical modules. This significantly reduces regression risk during large-scale refactoring and enhances the reliability and practicality of AI-assisted code restructuring.

This study addresses the imbalance in the test pyramid—characterized by an overreliance on coarse-grained integration and system tests, which leads to difficulties in fault localization and slow execution—by proposing, for the first time, a method to automatically generate unit tests from existing integration tests. The approach combines static and dynamic analysis to automatically isolate component dependencies and enhance coverage at the unit level. Implemented as a Node.js tool and evaluated on twelve open-source JavaScript projects, the technique produces high-quality unit tests that significantly improve test suite structure, thereby increasing both testing efficiency and maintainability.

This work addresses the limited accessibility of large language model (LLM) and agent workflow development for engineers without machine learning expertise, primarily due to the absence of integrated testing, debugging, and reproducibility capabilities. To bridge this gap, the authors propose a novel IDE-native AI observability workflow, implemented as the AI Toolkit plugin for JetBrains IDEs. This approach seamlessly embeds trace capture and evaluation into standard run/debug cycles, enabling automatic hierarchical trace logging during execution, one-click dataset persistence, and a pluggable, unit-test-like evaluation framework. By minimizing environment setup and context-switching overhead, the solution facilitates routine evaluation and immediate trace visualization. Empirical data from the initial PyCharm release demonstrates high adoption, sustained usage, and low churn, confirming that IDE-integrated tooling effectively lowers the barrier to entry for non-ML developers.

This work addresses the challenges faced by resource-constrained software startup teams with limited user experience (UX) expertise in efficiently creating and evaluating low-fidelity prototypes. To this end, we propose SoftBoard, a web-based multi-agent system that integrates large language model–driven intelligent agents into the prototyping workflow for the first time, enabling an end-to-end pipeline from requirement elicitation to automated generation of low-fidelity prototypes. The system incorporates an embedded evaluation mechanism based on usability heuristic rules and unifies prototype editing, team collaboration, and AI-assisted functionalities within a single platform, substantially reducing reliance on specialized UX knowledge. Preliminary feasibility studies demonstrate that SoftBoard effectively standardizes and streamlines the minimum viable product (MVP) development process.

This work addresses the challenge of balancing software quality, testability, and maintainability under rapid iteration and frequent requirement changes. It proposes Algorithm-Driven Development (ADD), a novel approach that unifies requirements specification and technical design by using algorithm flowcharts as a single, coherent artifact. This integration enables end-to-end modeling of requirements, architecture, and testing. Leveraging this model, the system automatically generates high-coverage acceptance tests and incorporates continuous integration with code coverage feedback. Industrial adoption at Dassault Systèmes demonstrates that ADD achieves over 95% code coverage, substantially reduces defect density, and ensures a stable delivery cadence, outperforming conventional test-driven development and test-after approaches.