Tootfinder

Measuring the effectiveness of code review comments in GitHub repositories: A machine learning approach
Shadikur Rahman, Umme Ayman Koana, Hasibul Karim Shanto, Mahmuda Akter, Chitra Roy, Aras M. Ismael
https://arxiv.org/abs/2508.16053

Measuring the effectiveness of code review comments in GitHub repositories: A machine learning approach
This paper illustrates an empirical study of the working efficiency of machine learning techniques in classifying code review text by semantic meaning. The code review comments from the source control repository in GitHub were extracted for development activity from the existing year for three open-source projects. Apart from that, programmers need to be aware of their code and point out their errors. In that case, it is a must to classify the sentiment polarity of the code review comments to a…

Hooray, parallel downloading of repository metadata was merged for #dnf! 🥳
https://github.com/rpm-software-management/dnf5/issues/307#issuecomment-2…

A Methodological Framework for LLM-Based Mining of Software Repositories
Vincenzo De Martino, Joel Casta\~no, Fabio Palomba, Xavier Franch, Silverio Mart\'inez-Fern\'andez
https://arxiv.org/abs/2508.02233

A Methodological Framework for LLM-Based Mining of Software Repositories
Large Language Models (LLMs) are increasingly used in software engineering research, offering new opportunities for automating repository mining tasks. However, despite their growing popularity, the methodological integration of LLMs into Mining Software Repositories (MSR) remains poorly understood. Existing studies tend to focus on specific capabilities or performance benchmarks, providing limited insight into how researchers utilize LLMs across the full research pipeline. To address this gap,…

Interoperable verification and dissemination of software assets in repositories using COAR Notify
Matteo Cancellieri, Martin Docekal, David Pride, Morane Gruenpeter, David Douard, Petr Knoth
https://arxiv.org/abs/2508.02335

Interoperable verification and dissemination of software assets in repositories using COAR Notify
The discoverability, attribution, and reusability of open research software are often hindered by its obscurity within academic manuscripts. To address this, the SoFAIR project (2024-2025) introduces a comprehensive workflow leveraging machine learning tools for extracting software mentions from research papers. The project integrates repository systems, authors, and services like HAL and Software Heritage to ensure proper archiving, citation, and accessibility of research software in alignment…

This https://arxiv.org/abs/2502.13681 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Repo2Run: Automated Building Executable Environment for Code Repository at Scale
Scaling up executable code data is significant for improving language models' software engineering capability. The intricate nature of the process makes it labor-intensive, time-consuming and expert-knowledge-dependent to build a large number of executable code repositories, limiting the scalability of existing work based on running tests. The primary bottleneck lies in the automated building of test environments for different repositories, which is an essential yet underexplored task. To mitig…

This https://arxiv.org/abs/2501.18160 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditing
Code auditing is the process of reviewing code with the aim of identifying bugs. Large Language Models (LLMs) have demonstrated promising capabilities for this task without requiring compilation, while also supporting user-friendly customization. However, auditing a code repository with LLMs poses significant challenges: limited context windows and hallucinations can degrade the quality of bug reports, and analyzing large-scale repositories incurs substantial time and token costs, hindering eff…