Modern Software Development for JUNO offline software

• URL: http://arxiv.org/abs/2309.13780v1
• Date: Mon, 25 Sep 2023 00:13:47 GMT
• Title: Modern Software Development for JUNO offline software
• Abstract summary: The Jiangmen Underground Neutrino Observatory (JUNO), under construction in South China, primarily aims to determine the neutrino mass hierarchy and to precise measure the neutrino oscillation parameters. The development of the JUNO offline software (JUNOSW) started in 2012, and it is quite challenging to maintain the JUNOSW for such a long time. New stringent requirements came out, such as how to reduce the building time for the whole project, how to deploy offline algorithms to an online environment, and how to improve the code quality with code review and continuous integration. This contribution will present the software development system based
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Jiangmen Underground Neutrino Observatory (JUNO), under construction in South China, primarily aims to determine the neutrino mass hierarchy and to precise measure the neutrino oscillation parameters. The data-taking is expected to start in 2024 and the detector plans to run for more than 20 years. The development of the JUNO offline software (JUNOSW) started in 2012, and it is quite challenging to maintain the JUNOSW for such a long time. In the last ten years, tools such as Subversion, Trac, and CMT had been adopted for software development. However, new stringent requirements came out, such as how to reduce the building time for the whole project, how to deploy offline algorithms to an online environment, and how to improve the code quality with code review and continuous integration. To meet the further requirements of software development, modern development tools are evaluated for JUNOSW, such as Git, GitLab, CMake, Docker, and Kubernetes. This contribution will present the software development system based on these modern tools for JUNOSW and the functionalities achieved: CMake macros are developed to simplify the build instructions for users; CMake generator expressions are used to control the build flags for the online and offline environments; a tool named git-junoenv is developed to help users partially checkout and build the software; a script is used to build and deploy the software on the CVMFS server; a Docker image with CVMFS client installed is created for continuous integration; a GitLab agent is set up to manage GitLab runners in Kubernetes with all the configurations in a GitLab repository.

Related papers

• ProgramBench: Can Language Models Rebuild Programs From Scratch? [59.40748183470308]
  ProgramBench measures the ability of software engineering agents to develop software holisitically.<n>End-to-end behavioral tests are generated via agent-driven fuzzing.<n>Models favor monolithic, single-file implementations that diverge sharply from human-written code.
  arXiv  Detail & Related papers  (2026-05-05T09:17:02Z)
• Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering [0.0]
  Modern agentic systems operate at the granularity of a repository, a feature, or an algorithm.<n>We propose a six-layer reference architecture for agentic software engineering systems.<n>We argue that the central object of inquiry has shifted from code generation to delegated execution under human supervision.
  arXiv  Detail & Related papers  (2026-04-29T04:06:47Z)
• Gym-Anything: Turn any Software into an Agent Environment [67.2443447990221]
  Gym-Anything is a framework for converting any software into an interactive computer-use environment.<n> CUA-World is a collection of over 10K long-horizon tasks spanning domains from medical science and astronomy to engineering and enterprise systems.
  arXiv  Detail & Related papers  (2026-04-07T17:38:15Z)
• DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle [84.01703913780946]
  Handling the DevOps cycle in real-world software requires analyzing large-scale projects, understanding dynamic program behaviors, leveraging domain-specific tools, and making sequential decisions.<n>We introduce DevOps-Gym, the first end-to-end benchmark for evaluating AI agents across core DevOps tasks.
  arXiv  Detail & Related papers  (2026-01-27T18:43:46Z)
• Empowering smart app development with SolidGPT: an edge-cloud hybrid AI agent framework [0.0]
  SolidGPT is an open-source, edge-cloud hybrid developer assistant built on GitHub.<n>It enables developers to: talk to your beginnings: interactively query and project structure.<n>It generates PRDs, task breakdowns, boards, and even web app scaffolds.
  arXiv  Detail & Related papers  (2025-12-09T06:34:28Z)
• A General Solution for the Implementation of CI/CD in Embedded Linux Development [0.6445605125467574]
  This research was conducted with the aim of designing and implementing an integrated and reproducible infrastructure for the development, building, and testing of a Linux-based operating system using the Yocto Project.<n>The proposed structure was implemented based on a three-layer architecture consisting of the main Yocto repositories, a custom layer (meta-custom), and a coordinating manifest layer.<n> Continuous Integration and Continuous Deployment pipelines were implemented with GitLab CI and combined with an isolated Docker environment to automate and streamline the build and testing.
  arXiv  Detail & Related papers  (2025-10-22T04:48:41Z)
• PIPer: On-Device Environment Setup via Online Reinforcement Learning [74.52354321028493]
  Automated environment setup methods could assist developers by providing fully configured environments for arbitrary repositories without manual effort.<n>Recent studies reveal that even state-of-the-art Large Language Models (LLMs) achieve limited success in automating this task.<n>We combine supervised fine-tuning for generating correct scripts and Reinforcement Learning with Verifiable Rewards (RLVR) to adapt it to the task of environment setup.<n>On EnvBench-Python, our method enables Qwen3-8B (a model runnable on consumer hardware) to perform on par with larger models-Qwen3-32B and GPT-4
  arXiv  Detail & Related papers  (2025-09-29T20:03:05Z)
• 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)
• Git Context Controller: Manage the Context of LLM-based Agents like Git [6.521644491529639]
  Large language model (LLM) based agents have shown impressive capabilities by interleaving internal reasoning with external tool use.<n>We introduce Git-Context-Controller (GCC), a structured context management framework inspired by software version control systems.<n>In a self-replication case study, a GCC-augmented agent builds a new CLI agent from scratch, achieving 40.7 task resolution, compared to only 11.7 without GCC.
  arXiv  Detail & Related papers  (2025-07-30T08:01:45Z)
• SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving [90.32201622392137]
  We present SwingArena, a competitive evaluation framework for Large Language Models (LLMs)<n>Unlike traditional static benchmarks, SwingArena models the collaborative process of software by pairing LLMs as iterations, who generate patches, and reviewers, who create test cases and verify the patches through continuous integration (CI) pipelines.
  arXiv  Detail & Related papers  (2025-05-29T18:28:02Z)
• EnvBench: A Benchmark for Automated Environment Setup [76.02998475135824]
  Large Language Models have enabled researchers to focus on practical repository-level tasks in software engineering domain. Existing studies on environment setup introduce innovative agentic strategies, but their evaluation is often based on small datasets. To address this gap, we introduce a comprehensive environment setup benchmark EnvBench.
  arXiv  Detail & Related papers  (2025-03-18T17:19:12Z)
• PyPackIT: Automated Research Software Engineering for Scientific Python Applications on GitHub [0.0]
  PyPackIT is a user-friendly, ready-to-use software that enables scientists to focus on the scientific aspects of their projects. PyPackIT offers a robust project infrastructure including a build-ready Python package skeleton, a fully operational documentation and test suite, and a control center for dynamic project management.
  arXiv  Detail & Related papers  (2025-03-06T19:41:55Z)
• LLM Agents Making Agent Tools [2.5529148902034637]
  Tool use has turned large language models (LLMs) into powerful agents that can perform complex multi-step tasks.<n>But these tools must be implemented in advance by human developers.<n>We propose ToolMaker, an agentic framework that autonomously transforms papers with code into LLM-compatible tools.
  arXiv  Detail & Related papers  (2025-02-17T11:44:11Z)
• Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
  We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
  arXiv  Detail & Related papers  (2024-10-02T09:11:10Z)
• OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
  We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer. We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T17:50:43Z)
• Securing Confidential Data For Distributed Software Development Teams: Encrypted Container File [0.0]
  Cloud-based version management services like GitHub are commonly used for source code and other files. A challenge arises when developers from different companies or organizations share the platform, as sensitive data should be encrypted to restrict access to certain developers only. This paper discusses existing tools addressing this issue, highlighting their shortcomings. The authors propose their own solution, Encrypted Container Files, designed to overcome the deficiencies observed in other tools.
  arXiv  Detail & Related papers  (2024-07-12T10:19:49Z)
• GitHub Marketplace for Automation and Innovation in Software Production [2.0749231618270803]
  GitHub Marketplace hosts automation tools to assist developers with the production of their GitHub-hosted projects. This study explores the platform's characteristics, features, and policies and identifies common themes in production automation.
  arXiv  Detail & Related papers  (2024-07-07T23:55:15Z)
• KGym: A Platform and Dataset to Benchmark Large Language Models on Linux Kernel Crash Resolution [59.20933707301566]
  Large Language Models (LLMs) are consistently improving at increasingly realistic software engineering (SE) tasks. In real-world software stacks, significant SE effort is spent developing foundational system software like the Linux kernel. To evaluate if ML models are useful while developing such large-scale systems-level software, we introduce kGym and kBench.
  arXiv  Detail & Related papers  (2024-07-02T21:44:22Z)
• AutoCodeRover: Autonomous Program Improvement [8.66280420062806]
  We propose an automated approach for solving GitHub issues to autonomously achieve program improvement. In our approach called AutoCodeRover, LLMs are combined with sophisticated code search capabilities, ultimately leading to a program modification or patch. Experiments on SWE-bench-lite (300 real-life GitHub issues) show increased efficacy in solving GitHub issues (19% on SWE-bench-lite), which is higher than the efficacy of the recently reported SWE-agent.
  arXiv  Detail & Related papers  (2024-04-08T11:55:09Z)
• DevBench: A Comprehensive Benchmark for Software Development [72.24266814625685]
  DevBench is a benchmark that evaluates large language models (LLMs) across various stages of the software development lifecycle. Empirical studies show that current LLMs, including GPT-4-Turbo, fail to solve the challenges presented within DevBench. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications.
  arXiv  Detail & Related papers  (2024-03-13T15:13:44Z)
• GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension [81.44231422624055]
  A growing area of research focuses on Large Language Models (LLMs) equipped with external tools capable of performing diverse tasks. In this paper, we introduce GitAgent, an agent capable of achieving the autonomous tool extension from GitHub.
  arXiv  Detail & Related papers  (2023-12-28T15:47:30Z)
• Testing GitHub projects on custom resources using unprivileged Kubernetes runners [1.137903861863692]
  GitHub is a popular repository for hosting software projects. Native GitHub Actions make it easy for software developers to validate new commits and have confidence that new code does not introduce major bugs. The freely available test environments are limited to only a few popular setups but can be extended with custom Action Runners.
  arXiv  Detail & Related papers  (2023-05-17T16:31:41Z)
• The GitHub Development Workflow Automation Ecosystems [47.818229204130596]
  Large-scale software development has become a highly collaborative endeavour. This chapter explores the ecosystems of development bots and GitHub Actions. It provides an extensive survey of the state-of-the-art in this domain.
  arXiv  Detail & Related papers  (2023-05-08T15:24:23Z)

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.