flask

To address the difficulty non-technical users face in performing web scraping, this paper proposes and implements a low-barrier, fully automated web application. The system adopts a modular three-stage architecture—acquisition, extraction, and execution—integrated with a visual interface and guided workflow to minimize usability complexity. It incorporates user authentication, task history management, and MongoDB-based persistent storage to ensure security and personalization. Built on Flask, it leverages requests for HTTP handling, BeautifulSoup for HTML parsing, and regular expressions for robust pattern matching, while supporting one-click export of structured data to CSV. Experimental evaluation demonstrates that users with no programming background can complete end-to-end data collection, cleaning, and download from target web pages within minutes. This significantly improves data acquisition efficiency and accessibility, bridging the gap between lightweight automation and broad usability in web crawling tools.

This paper addresses the integration bottleneck of Fluid Construction Grammar (FCG) in mainstream programming environments by introducing PyFCG—the first complete, efficient, and scalable native Python implementation. Leveraging object-oriented design, dynamic grammar parsing, a hybrid symbolic-probabilistic representation, and a graph-based construction-matching engine, PyFCG enables seamless interoperability with key Python ecosystem libraries such as NumPy and NLTK. Its core contributions are threefold: (1) an open-source, unified framework for FCG modeling and execution; (2) corpus-driven, usage-based construction learning grounded in empirical linguistic data; and (3) support for multi-agent simulations of language evolution. All components are publicly available under an open-source license and accompanied by interactive tutorials. By unifying formal grammatical theory with modern computational infrastructure, PyFCG substantially lowers the barrier to entry for construction grammar modeling and computational linguistics experimentation.

This work addresses the redundancy and inconsistency arising from maintaining separate HTTP endpoints and Model Context Protocol (MCP) tool registrations in current large language model (LLM) tool development, which leads to duplicated business logic and boilerplate code. To resolve this, the authors propose a skill-centric unified framework that treats the skill registry as the single source of truth. Leveraging Pydantic type annotations and content negotiation, the framework dynamically generates FastAPI endpoints with Server-Sent Events (SSE) streaming support, interactive OpenAPI documentation, and zero-configuration MCP tools. This approach ensures seamless compatibility with both streaming and non-streaming clients while preserving type consistency. Evaluation across six representative skills demonstrates a 74% reduction in boilerplate code compared to manual dual-stack implementations, without sacrificing any capabilities of the FastAPI ecosystem.

Existing Python library migration tools are largely restricted to API-level mapping or specific library pairs, suffering from narrow coverage and low automation. This paper introduces the first end-to-end, general-purpose Python library migration framework, leveraging large language models (LLMs) as its core engine and integrating static analysis (to extract contextual dependencies) with dynamic analysis (to verify behavioral consistency), enabling fully automated code migration between functionally similar libraries. We propose a novel program-analysis-driven LLM post-processing optimization mechanism, significantly enhancing migration accuracy and robustness. Our command-line implementation is evaluated on 717 real-world projects: 32% achieve fully correct migrations, and in over 50% of projects, developers need to address fewer than 14% of residual changes—substantially reducing manual effort and error rates.

This study addresses the high technical barrier of traditional web scraping, which limits non-expert users’ ability to extract data from complex websites. For the first time, it systematically evaluates two large language model (LLM)-enabled scraping paradigms—LLM-assisted script generation and end-to-end autonomous agents—from the perspective of non-expert users across 35 websites with varying security levels. Experimental results demonstrate that, using off-the-shelf LLM tools, a single prompt combined with minimal iteration (typically fewer than five attempts) effectively overcomes challenges such as authentication, anti-bot mechanisms, and even CAPTCHA. End-to-end agents substantially lower the barrier for scraping complex sites, while LLM-assisted scripting proves more efficient for static pages, together advancing the democratization of data access.

This study addresses the high development costs and significant code redundancy associated with traditional institutional mobile applications that rely heavily on native Android development. To overcome these limitations, the authors propose a full-stack solution leveraging a Django backend and an HTMX frontend, integrated via a WebView bridge to deliver a campus management system without writing any Android SDK code. The system supports core functionalities including task scheduling, inventory management, and attendance tracking, and is deployed using a self-hosted Docker Compose setup, eliminating dependence on external cloud services. Evaluated in a real-world institutional setting, this approach demonstrates for the first time that HTMX combined with Django can effectively replace conventional APK-based development, achieving a 54% reduction in development time, a 91% decrease in HTTP payload size, and a user satisfaction score of 4.2 out of 5.0 among 42 participants.

Low-resource software frameworks (e.g., HarmonyOS) suffer from poor code generation performance by large language models (LLMs) due to insufficient pretraining exposure to framework-specific APIs and syntax. To address this, we propose an API knowledge graph–driven data construction and fine-tuning methodology. Our approach innovatively integrates an API knowledge graph with uncertainty estimation and Monte Carlo Tree Search (MCTS) to automatically generate high-quality single- and multi-API question–answer pairs—without requiring executable code. This yields the first HarmonyOS-specific code generation benchmark dataset. Experiments based on the Qwen model demonstrate that fine-tuning on our API-oriented data improves pass@1 accuracy from 17.59% to 25.00%, substantiating both the effectiveness and generalizability of API-centric data for enhancing LLMs’ code generation capabilities in low-resource frameworks.

This work proposes an automated approach leveraging large language models (LLMs) to migrate unittest-based test suites in Python projects to Pytest, aiming to reduce manual effort and accelerate test modernization. We introduce the first real-world dataset for unittest-to-Pytest migration and systematically evaluate the performance of GPT-4o and Claude Sonnet 4 under zero-shot, one-shot, and chain-of-thought prompting strategies. Experimental results show that 48.5% of LLM-generated migrations pass the original test suite: GPT-4o tends to aggressively refactor tests into a functional style, whereas Claude Sonnet 4 conservatively preserves the original class-based structure. These findings demonstrate the potential of LLMs as assistants in test migration tasks while underscoring the necessity of careful validation of their outputs.

This work addresses the significant token redundancy in existing OpenAPI specifications when used in large language model (LLM) contexts, which hampers inference efficiency. To mitigate this, the authors propose LAPIS—a lightweight, LLM-optimized API specification that substantially reduces context length while preserving essential semantics. LAPIS achieves this through domain-specific constructs such as centralized error definitions, webhook trigger conditions, structured rate-limiting policies, and declarative operation workflows. Designed as a domain-specific language, LAPIS supports automatic conversion from OpenAPI 3.x without requiring a custom parser and remains compatible with the cl100k_base tokenizer. Experiments on five real-world APIs—including GitHub and Twilio—demonstrate that LAPIS reduces token consumption by 85.5% and 88.6% on average compared to OpenAPI YAML and JSON representations, respectively.

This study addresses the lack of systematic understanding regarding the application domains, maintenance characteristics, and effective design practices of GitHub template repositories. Conducting the first large-scale empirical investigation, the work integrates data mining, statistical analysis, code quality assessment tools—detecting code smells, vulnerabilities, and security hotspots—and an LLM-as-a-judge classification approach to systematically uncover domain distributions, language-specific quality variations, and maintenance patterns. The findings reveal web development as the dominant application domain, with high-quality templates consistently adhering to software engineering best practices and offering comprehensive documentation. Through qualitative evaluation, the study distills actionable design guidelines and identifies common pitfalls, providing practical guidance for developers creating or using template repositories.