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About 7,302 papers for “Codebase”

ObjectRL: An Object-Oriented Reinforcement Learning Codebase

Gulcin Baykal, Abdullah Akgül, Manuel Haußmann et al. · ArXiv.org · 2025

ObjectRL is an open-source Python codebase for deep reinforcement learning (RL), designed for research-oriented prototyping with minimal programming effort. Unlike existing codebases, ObjectRL is built on Object-Oriented Programming (OOP) principles, providing a clear structure that simplifies the implementation, modification, and evaluation of new algorithms. ObjectRL lowers the entry barrier for deep RL research by organizing best practices into explicit, clearly separated components, making them easier to understand and adapt. Each algorithmic component is a class with attributes that describe key RL concepts and methods that intuitively reflect their interactions. The class hierarchy closely follows common ontological relationships, enabling data encapsulation, inheritance, and polymorphism, which are core features of OOP. We demonstrate the efficiency of ObjectRL's design through representative use cases that highlight its flexibility and suitability for rapid prototyping. The documentation and source code are available at https://objectrl.readthedocs.io and https://github.com/adinlab/objectrl .

Cited by 0Open access

Increasing the modularity of Web Pentimento's codebase

Halla M Moore · DSpace@MIT (Massachusetts Institute of Technology) · 2016

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.

Cited by 0Open access

epinowcast/baselinenowcast-paper: Updated codebase for v2

Kaitlyn E. Johnson, Sam Abbott, Johannes Bracher · Zenodo (CERN European Organization for Nuclear Research) · 2026

What's Changed Issue 133: tweak supplement by @kaitejohnson in https://github.com/epinowcast/baselinenowcast-paper/pull/134 Hot fix add changes to the supplement by @kaitejohnson in https://github.com/epinowcast/baselinenowcast-paper/pull/138 Update codemeta.json by @github-actions[bot] in https://github.com/epinowcast/baselinenowcast-paper/pull/135 Change color scheme by @kaitejohnson in https://github.com/epinowcast/baselinenowcast-paper/pull/139 Full Changelog: https://github.com/epinowcast/baselinenowcast-paper/compare/v1.0.1-pre-publication-update...v2.0

Cited by 1Open access

Gistify! Codebase-Level Understanding via Runtime Execution

Lee, Hyunji, Kim, Minseon, Singh, Chinmay et al. · ArXiv.org · 2025

As coding agents are increasingly deployed in large codebases, the need to automatically design challenging, codebase-level evaluation is central. We propose Gistify, a task where a coding LLM must create a single, minimal, self-contained file that can reproduce a specific functionality of a codebase. The coding LLM is given full access to a codebase along with a specific entrypoint (e.g., a python command), and the generated file must replicate the output of the same command ran under the full codebase, while containing only the essential components necessary to execute the provided command. Success on Gistify requires both structural understanding of the codebase, accurate modeling of its execution flow as well as the ability to produce potentially large code patches. Our findings show that current state-of-the-art models struggle to reliably solve Gistify tasks, especially ones with long executions traces.

Cited by 0Open access

Elements: ALE-AMR Framework and the PISALE Codebase

Koniges, Alice, Eder, David, Lee, Jonghyun · Figshare · 2023

The solution of partial differential equations (PDEs) on modern HPC platforms is essential to the continued success of research and modeling for a wide variety of areas, especially groundwater flow and transport modeling in Pacific islands. The project implements an innovative combination of advanced mathematical techniques of Arbitrary Lagrangian Eulerian (ALE) methods with Adaptive Mesh Refinement (AMR), including parallel software tools to dynamically adapt the grids and special Lagrangian-flow methods that allow for the simulation of complex regional groundwater flow with dynamic freshwater and seawater interaction in heterogeneous volcanic rocks. The PISALE (Pacific Island Structured-AMR with ALE) software aims at a publicly available sustainable branch of the software and will provide accurate and scalable simulations of complex groundwater flow processes in the Hawaiian islands. The status of our ongoing project will be presented with student involvement, course development, and future plans

Cited by 0Open access

Bizbalt/RAFT-knowledgebase: Codebase for the RAFT-knowledgebase

Yannik Köster, MichaelRingleb · Zenodo (CERN European Organization for Nuclear Research) · 2025

This is the first release associated with the publication of the codebase of the publication "Unveiling the knowledge of a RAFT polymerizations database obtained from an automated parallel synthesizer". Displaying the results of automated synthesis as an interactive website. An amendment to the FAIR principles.

Cited by 0Open access

Porting Stubbur tickets app to React Native codebase

Tjörvi Óskarsson 1980-, Arnar Ægisson 1974-, Arnar Daníel Jónsson 1982- et al. · Skemman · 2025

The primary objective of this project is to modernize Stubbur’s mobile application by consolidating platform-specific codebases into a unified React Native solution. This strategic shift aims to reduce technical overhead, improve long-term maintainability, and support faster feature delivery. By aligning the app with current cross-platform development practices, the project also seeks to improve development efficiency and create a more consistent experience for end users.

Cited by 0

Taking code analysis into use for existing codebase

Uitto, Janne · Tampere University Institutional Repository (Tampere University) · 2020

The amount of the code increases in applications across industries. Also, the amount of vulnerabilities has increased, which increases the importance of quality assurance. Any kind of quality assurance increases the probability of finding the issues from programs. One method to detect security vulnerabilities is to run code analysis. Code analysis tools can automatically detect potential security vulnerabilities from source code including overruns, using uninitialised memory, null pointer dereferences, and memory leaks.
\nThis thesis shows how a code analysis tool can be included as part of a company's existing software development process so that new issues can be found as early as possible. Code analysis tools might report many findings from codebases that have not been analysed previously. Existing issues need to be fixed so that new issues can be identified easily. The effort on fixing existing issues was compared to the severity of found issues, which then gave the cost-effectiveness of the effort spent on fixing.
\nA code analysis tool was taken into use at M-Files. The code analysis tool reported a total of 3,064 existing issues from the company's codebase. Found issues were categorised into three severity levels: 3,054 low, 3 high, and 7 critical issues were found. Found issues were fixed. It took 60.5 work hours to fix these issues.
\nA CI pipeline is used to automate integration steps for the new code. After existing issues were fixed the code analysis tool was set to monitor all new changes in the company's CI pipeline. Currently, it is mandatory to have a clean analysis result before new changes can be merged into the main code branch. This reduces the number of issues, including vulnerabilities, in released software.
\nIt is not mandatory to have a code analysis tool to verify codebase from the beginning of the project. This thesis shows that it was a tolerable effort to take code analysis into use for the existing codebase. Setting up code analysis as part of the development process as soon as possible is a recommended action for all organisations.

Cited by 0Open access

lf-santos/NG-2022crisis-LCA: Codebase (without results)

Lucas Francisco dos Santos · Zenodo (CERN European Organization for Nuclear Research) · 2024

Codebase to reproduce the results in the scientific article Santos et al. (2024), Environmental impacts of restructuring the EU's natural gas supply and consumption: Learnings from the 2022 energy crisis.

Cited by 0Open access

Elements: ALE-AMR Framework and the PISALE Codebase

Koniges, Alice, Eder, David, Lee, Jonghyun · Figshare · 2023

The solution of partial differential equations (PDEs) on modern HPC platforms is essential to the continued success of research and modeling for a wide variety of areas, especially groundwater flow and transport modeling in Pacific islands. The project implements an innovative combination of advanced mathematical techniques of Arbitrary Lagrangian Eulerian (ALE) methods with Adaptive Mesh Refinement (AMR), including parallel software tools to dynamically adapt the grids and special Lagrangian-flow methods that allow for the simulation of complex regional groundwater flow with dynamic freshwater and seawater interaction in heterogeneous volcanic rocks. The PISALE (Pacific Island Structured-AMR with ALE) software aims at a publicly available sustainable branch of the software and will provide accurate and scalable simulations of complex groundwater flow processes in the Hawaiian islands. The status of our ongoing project will be presented with student involvement, course development, and future plans

Cited by 0Open access

Optimizing Compilation Times for an Elixir Monolithic Codebase

Lívia Barbosa · 2025

An inefficiently compiled codebase can harm the developer experience, the continuous integration process, and increase organizational costs. Addressing this problem may be challenging, requiring expertise and time to identify, resolve, and prevent the concerns. This paper presents how Remote manages the compilation time of its large-scale monolithic codebase.

Cited by 0Open access