arXiv AI

CIDR: A Large-Scale Industrial Source Code Dataset for Software Engineering Research

arXiv:2605. 12153v2 Announce Type: replace-cross Abstract: We present the Curated Industrial Developer Repository (CIDR), a large-scale dataset of real-world software repositories collected from industrial partners.

arXiv Machine Learning
Aug 6

A Comprehensive Evaluation of Code Language Models for Security Patch Detection

arXiv:2605. 13138v2 Announce Type: replace-cross Abstract: Automated detection of vulnerability-fixing commits (\vfcs) is critical for timely security patch deployment, as advisory databases lag patch releases by a median of 25 days and many fixes never receive advisories.

By Nils Loose, Joseph Bienh\"uls, Kristoffer Hempel, Felix M\"achtle, Thomas Eisenbarth
arXiv AI
Jul 14

SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks

arXiv:2507. 11059v3 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset.

By Pavel Adamenko, Mikhail Ivanov, Aidar Valeev, Rodion Levichev, Pavel Zadorozhny, Ivan Lopatin, Dmitry Babaev, Alena Fenogenova, Valentin Malykh
arXiv AI
Jun 17

ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software

arXiv:2606. 17283v1 Announce Type: cross Abstract: Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others.

By Xiang Mei, Jordi Del Castillo, Pulkit Singh Singaria, Haoran Xi, Abdelouahab Benchikh, Tiffany Bao, Ruoyu Wang, Yan Shoshitaishvili, Adam Doup\'e, Hammond Pearce, Brendan Dolan-Gavitt
arXiv AI
Jun 30

SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

arXiv:2511. 06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness.

By Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh, Kevin Swersky, Ofir Press, Enhui Li, Vijay Janapa Reddi, Parthasarathy Ranganathan
arXiv AI
Jun 9

Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets

arXiv:2605. 28510v2 Announce Type: replace-cross Abstract: Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training examples verbatim and without authorship attribution, raising legal and ethical concerns around plagiarism and license compliance.

By Andrea Gurioli, Davide D'Ascenzo, Federico Pennino, Maurizio Gabbrielli, Stefano Zacchiroli