web application development

Current prompt engineering for large language models faces challenges including loosely structured specifications, difficulty in integrating multimodal data, tight coupling of content and presentation, and lack of collaborative tooling. To address these, we propose POML—a domain-specific markup language for complex prompt orchestration. POML innovatively integrates component-based tagging, CSS-like styling directives, and dynamic templating to decouple content from presentation; enables unified, structured modeling and management of multimodal inputs (e.g., documents, tables, images); and is supported by an integrated development environment (IDE) plugin, software development kit (SDK), and version-controlled toolchain for collaborative development and integrated debugging. Empirical evaluation on PomLink and TableQA tasks demonstrates that POML improves prompt construction efficiency by 42% and accuracy by 31%. To our knowledge, POML is the first language-level infrastructure offering systematic, extensible support for industrial-scale prompt engineering.

This work addresses the labor-intensive nature of presentation tasks—such as formatting and layout—in dashboard authoring, which currently lack support for partial reuse. Through a systematic user study, we characterize the needs and challenges associated with cross-source reuse of visual presentation elements. Building on these insights, we propose a novel paradigm that enables partial reuse of styles and layouts from multiple existing dashboards. We design and implement ReDash, a prototype system embodying this approach, and demonstrate through proof-of-concept experiments that our mechanism effectively overcomes key barriers in common reuse scenarios. The results show a significant improvement in authoring efficiency, confirming the feasibility and practical potential of partial reuse in real-world dashboard creation.

Developing cross-platform graphical user interfaces (GUIs) and plugins for command-line tools in structural bioinformatics is often costly and complex. This work proposes a three-stage automated workflow that leverages a platform-agnostic formal GUI specification, decouples model, view, and presenter components through the Model–View–Presenter (MVP) architectural pattern, and employs a dedicated code generator to automatically produce plugins for target platforms—namely VMD, PyMOL, and the web. To the best of our knowledge, this is the first systematic application of the MVP pattern to the automatic GUI generation for CLI tools, substantially enhancing logic reusability, cross-platform portability, and development efficiency. The framework’s generality, extensibility, and practical utility are demonstrated by successfully generating plugins for multiple tools from the Structural Bioinformatics Library across all three platforms.

Community-driven scientific workflow ecosystems often struggle to sustain themselves due to ambiguous maintenance and user support mechanisms, particularly in cross-platform collaboration and heterogeneous execution environments. This study presents the first cross-platform empirical analysis of the nf-core ecosystem, systematically examining 15,760 GitHub issues, 35,411 pull requests, and 895 forum discussions. By integrating metadata and textual features into predictive models, the research uncovers significant disparities in maintenance and support activities across platforms and highlights weak explicit linkages among them. The findings reveal that issues, pull requests, and forum posts predominantly serve distinct roles—coordinating maintenance, facilitating code integration, and providing user support, respectively. Moreover, issue actionability, diagnostic evidence, and depth of interaction emerge as critical determinants of resolution efficiency.

Non-structured, inconsistent, and incomplete REST API documentation impedes automated tool construction by AI systems, necessitating costly manual intervention. Method: We propose the first automated framework for generating tools from API documentation, featuring (1) an LLM-driven pipeline for multi-format document understanding and structured extraction; (2) a verification-augmented knowledge base enabling inference-based completion of missing parameters and response fields; and (3) the first open-source API extraction benchmark—comprising 167 documents and 744 endpoints—with fine-grained JSON Schema annotations. Results: Our method achieves significant improvements over baselines on the API Extraction Benchmark. It has been successfully deployed in a domain-specific AI agent for glycobiology research. The open-source toolchain enables zero-shot, rapid integration of scientific REST APIs into AI workflows.

This work addresses the limitation of existing text-to-process modeling approaches, which predominantly focus on control flow while neglecting resource and collaboration perspectives, thereby struggling to generate complete multi-party models. To overcome this, the authors propose a resource-aware generative pipeline that systematically incorporates the resource dimension into large language model (LLM)-driven process modeling for the first time. The method automatically constructs BPMN 2.0 collaboration diagrams from natural language descriptions, explicitly capturing organizational pools, role-based lanes, and inter-organizational message events, and employs an orthogonal layout algorithm for automated diagram arrangement. Experimental results across ten business processes and nine LLMs demonstrate that the approach accurately extracts resource-related information, maintains high control-flow quality, and incurs only minimal runtime overhead, advancing generative process modeling toward more collaborative and resource-complete representations.

This work addresses the persistent challenge of inconsistent development and execution environments faced by researchers operating across heterogeneous computing platforms—ranging from laptops and workstations to supercomputers and cloud infrastructures. To overcome this, the authors propose a modular and portable software ecosystem featuring a unified command-line interface that enables seamless orchestration and execution of scientific workflows. The system ensures cross-platform consistency, reproducibility, and scalability, thereby streamlining computational research across diverse hardware configurations. Its practical efficacy has been demonstrated through successful integration into the plan4res project under the European Union’s Horizon 2020 initiative, where it effectively supported complex, large-scale scientific workflows in varied computing environments.

This work addresses the disconnect between modular application design and execution in edge and cloud computing, particularly the challenges of uniformly modeling computational units, data sharing, and event dependencies. To bridge this gap, the paper proposes a domain-specific visual graph editor that enables users to define data and control flows through three core abstractions: kernel functions, shared memory nodes, and event triggers. The tool automatically generates deployable, machine-readable representations from these visual models. By integrating explicit execution semantics, modular design, and one-click deployment within a unified interface—combining visual modeling, domain-specific language (DSL) abstractions, event-driven architecture, and distributed shared memory—it significantly enhances the comprehensibility of execution order and dependencies. Evaluations in scenarios such as federated learning demonstrate its superior semantic expressiveness and direct deployability compared to general-purpose diagramming tools and conventional workflow editors.

Existing tool interfaces based on static endpoints struggle to express long-running workflows involving complex control flows such as loops, conditional branches, and retries. This work proposes replacing static endpoints with executable tool programs, enabling explicit effect typing and sophisticated workflow control through constraint-guided program construction, effect-aware exactly-once replay mechanisms, and configuration-driven execution policies. Implemented atop MCP-style services and a WebAssembly sandbox, the system demonstrates significant performance improvements in real-world scenarios, reducing end-to-end latency by up to 53.4% and client-side traffic by as much as 96.1%, with particularly pronounced gains under high network latency or increased workflow complexity.

This study addresses the unique challenges faced by non-developer-led knowledge communities—such as Obsidian—in maintaining sustainable plugin ecosystems, a domain lacking systematic understanding. Through an empirical investigation of 396 plugins, combining repository mining, large language model–driven topic modeling, and pull request analysis, this work presents the first systematic characterization of such ecosystems. It identifies six functional theme categories and reveals active software evolution patterns. Furthermore, the research proposes an analytical framework tailored for plugin maintenance in mixed-skill communities, articulating three strategic directions and six key research questions. The findings demonstrate that even non-developer communities can cultivate plugin ecosystems with discernible engineering structure and long-term sustainability.