arXiv AI

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

arXiv:2506. 11066v3 Announce Type: replace-cross Abstract: Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging.

arXiv AI

Jun 16

AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion

arXiv:2601. 19697v2 Announce Type: replace-cross Abstract: Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context and domain knowledge.

By Tianyue Jiang, Yanli Wang, Yanlin Wang, Daya Guo, Ensheng Shi, Yuchi Ma, Jiachi Chen, Zibin Zheng

llms rag reinforcement-learning benchmarks

arXiv Machine Learning

Jun 29

Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval

arXiv:2606. 27401v1 Announce Type: cross Abstract: Semantic code search and clone detection are essential for software development, maintenance, and reuse.

By Leonardo Venuta, Francesco Tosoni, Paolo Ferragina

llms benchmarks

arXiv AI

Jul 3

XSearch: Explainable Code Search via Concept-to-Code Alignment

arXiv:2605. 16046v2 Announce Type: replace-cross Abstract: Semantic code search has been widely adopted in both academia and industry.

By Yiming Liu, Ruofan Liu, Yun Lin, Zicong Zhang, Weiyu Kong, Pengnian Qi, Xiao Cheng, Weinan Zhang, Qianxiang Wang, Linpeng Huang

rag benchmarks safety

arXiv AI

Jul 17

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality

arXiv:2607. 14816v1 Announce Type: cross Abstract: Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias.

By Saima Afrin, Alessandro Midolo, Camilo Escobar-Vel\'asquez, Mario Linares-V\'asquez, Weiyuan Ding, Bowen Xu, Massimiliano Di Penta, Antonio Mastropaolo

llms benchmarks safety

arXiv Machine Learning

Aug 11

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring

arXiv:2605. 00754v4 Announce Type: replace-cross Abstract: Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scaling.

By Indraneil Paul, Goran Glava\v{s}, Iryna Gurevych

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 12

HybridCodeAuthorship: A Benchmark Dataset for Line-Level Code Authorship Detection

arXiv:2606. 12620v1 Announce Type: cross Abstract: Thanks to the rapid adoption of AI code assistants powered by large language models (LLMs), industry codebases are, increasingly, a hybrid of AI- and human-authored code.

By Luke Patterson, Li Wang, Adam Faulkner

llms benchmarks

arXiv AI

Jul 8

Scientific Code Search at Scale: A Multi-Domain Dataset and Benchmark

arXiv:2607. 05443v1 Announce Type: cross Abstract: Scientists increasingly rely on open-source tools to support their research workflows, yet discovering relevant software among over 600 million GitHub repositories remains challenging.

By Nishan Pantha, Pranath Reddy Kumbam, Sajil Awale, Pushwitha Krishnappa, Muthukumaran Ramasubramanian, Nidhi Jha, Emily Foshee, Ankur Kumar, Rachel Slank, Ashkbiz Danehkar, Rahul Ramachandran

benchmarks

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

llms rag

arXiv AI

Jul 13

Practical Source Code Recovery from Binary Functions Using Anchor-Based Retrieval and LLM Reasoning

arXiv:2607. 09452v1 Announce Type: cross Abstract: We present a practical pipeline for recovering source code from stripped binary functions by combining reverse engineering, anchor-based source code retrieval, and large language model reasoning.

By Charles Edward Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung

llms

Hugging Face Trending Papers

Jul 16

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality

Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored.

llms benchmarks safety

arXiv AI

Jul 29

Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation

arXiv:2607. 24884v1 Announce Type: cross Abstract: Repository-level code generation relies on heterogeneous evidence whose relevance, compatibility, and completeness are inherently uncertain.

By Chandan Kumar Sah, Xiaoli Lian, Li Zhang

llms rag

arXiv AI

2d ago

The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion

arXiv:2508. 16131v3 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context.

By Zoe Kotti, Konstantina Dritsa, Diomidis Spinellis, Panos Louridas

llms fine-tuning safety

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