Generating CodeMeta through declarative mapping rules: An open-ended approach using ShExML

• URL: http://arxiv.org/abs/2510.09172v2
• Date: Fri, 07 Nov 2025 16:16:03 GMT
• Title: Generating CodeMeta through declarative mapping rules: An open-ended approach using ShExML
• Abstract summary: This work proposes the use of declarative mapping rules to generate CodeMeta files for research software artefacts.<n>The generated CodeMeta files are validated using SHACL and ShEx and the whole generation workflow is automated.<n>This work can be used as an example upon which other developers can include a CodeMeta generation workflow in their repositories.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowadays, software is one of the cornerstones when conducting research in several scientific fields which employ computer-based methodologies to answer new research questions. However, for these experiments to be completely reproducible, research software should comply with the FAIR principles, yet its metadata can be represented following different data models and spread across different locations. In order to bring some cohesion to the field, CodeMeta was proposed as a vocabulary to represent research software metadata in a unified and standardised manner. While existing tools can help users to generate CodeMeta files for some specific use cases, they fall short on flexibility and adaptability. Hence, in this work, I propose the use of declarative mapping rules to generate CodeMeta files, illustrated through the implementation of three crosswalks in ShExML which are then expanded and merged to cover the generation of CodeMeta files for two existing research software artefacts. Moreover, the outputs are validated using SHACL and ShEx and the whole generation workflow is automated requiring minimal user intervention upon a new version release. This work can, therefore, be used as an example upon which other developers can include a CodeMeta generation workflow in their repositories, facilitating the adoption of CodeMeta and, ultimately, increasing research software FAIRness.

Related papers

• ToFu: A White-Box, Token-Efficient Agent Harness for Researchers [73.72929185946842]
  ToFu is an agentic harness for researchers that reads your files, edits files, runs commands, and integrates with your development tools.<n>As a research assistant, it supports practical research with superior token efficiency, lower cost, and multilingual capability.<n>As a research object, ToFu provides a white-box agentic harness that allows researchers to inspect, modify, and evaluate its orchestration logic, tool-use behavior, and harness design.
  arXiv  Detail & Related papers  (2026-07-13T11:26:06Z)
• CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents [43.426809750160665]
  We show that a coding agent equipped with nothing more than a standard Unix terminal can be trained to achieve strong results.<n>Our work particularly focuses on techniques for re-purposing existing coding agent environments for code search, reward design, and RL optimization.<n>We release the resulting model family, CodeScout, along with all our code and data for the community to build upon.
  arXiv  Detail & Related papers  (2026-03-18T15:25:42Z)
• Code2MCP: Transforming Code Repositories into MCP Services [53.234097255779744]
  Model Context Protocol (MCP) aims to create a standard for how Large Language Models use tools.<n>We introduce Code2MCP, an agent-based framework that automatically transforms a GitHub repository into a functional MCP service.<n>We demonstrate that Code2MCP successfully transforms open-source computing libraries in scientific fields such as bioinformatics, mathematics, and fluid dynamics.
  arXiv  Detail & Related papers  (2025-09-07T06:13:25Z)
• AutoP2C: An LLM-Based Agent Framework for Code Repository Generation from Multimodal Content in Academic Papers [9.851681616116718]
  We introduce Paper-to-Code'' (P2C), a novel task that transforms the multimodal content of scientific publications into fully executable code repositories.<n>We propose AutoP2C, a multi-agent framework based on large language models that processes both textual and visual content from research papers to generate complete code repositories.
  arXiv  Detail & Related papers  (2025-04-28T05:47:37Z)
• Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning [57.09163579304332]
  We introduce PaperCoder, a framework that transforms machine learning papers into functional code repositories.<n>PaperCoder operates in three stages: planning, designs the system architecture with diagrams, identifies file dependencies, and generates configuration files.<n>We then evaluate PaperCoder on generating code implementations from machine learning papers based on both model-based and human evaluations.
  arXiv  Detail & Related papers  (2025-04-24T01:57:01Z)
• CodeRAG: Supportive Code Retrieval on Bigraph for Real-World Code Generation [69.684886175768]
  Large language models (LLMs) have shown promising performance in automated code generation.<n>In this paper, we propose CodeRAG, a retrieval-augmented code generation framework.<n> Experiments show that CodeRAG achieves significant improvements compared to no RAG scenarios.
  arXiv  Detail & Related papers  (2025-04-14T09:51:23Z)
• Extending and Applying Automated HERMES Software Publication Workflows [0.6157382820537718]
  HERMES is a tool that automates the publication of software with rich metadata.<n>We show how to use HERMES as an end user, both via the informal command line interface, and as a step in a continuous integration pipeline.
  arXiv  Detail & Related papers  (2024-10-23T07:11:48Z)
• SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer [0.0]
  SuperCoder2.0 is an advanced autonomous system designed to enhance software development through artificial intelligence. System combines an AI-native development approach with intelligent agents to enable fully autonomous coding.
  arXiv  Detail & Related papers  (2024-09-17T13:44:42Z)
• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• Executable Code Actions Elicit Better LLM Agents [76.95566120678787]
  This work proposes to use Python code to consolidate Large Language Model (LLM) agents' actions into a unified action space (CodeAct) integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. The encouraging performance of CodeAct motivates us to build an open-source LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language.
  arXiv  Detail & Related papers  (2024-02-01T21:38:58Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.