arXiv Machine Learning

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.

arXiv AI

Jun 8

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.

By Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Walter Pretschner, Heinz Koeppl, Fakhri Karray

benchmarks

Hugging Face Trending Papers

Jul 29

MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entities

Code search in large-scale ecosystems is often hindered by the lexical gap between user queries and implementation details, alongside the trade-off between the low latency of traditional Information Retrieval (IR) and the precision of Deep Learning (DL). We present MediaWiki Code2Code Search, a neural retrieval system for semantic code-to-code discovery.

benchmarks

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 AI

Jul 31

MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entities

arXiv:2607. 26766v1 Announce Type: cross Abstract: Code search in large-scale ecosystems is often hindered by the lexical gap between user queries and implementation details, alongside the trade-off between the low latency of traditional Information Retrieval (IR) and the precision of Deep Learning (DL).

By Francesco Tosoni

benchmarks

arXiv AI

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning 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

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

1d ago

Efficient Code Embeddings from Code Generation Models

arXiv:2508. 21290v2 Announce Type: replace-cross Abstract: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages.

By Daria Kryvosheieva, Saba Sturua, Michael G\"unther, Han Xiao

rag benchmarks

arXiv AI

Jul 28

CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases

arXiv:2408. 03910v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and MBPP, but struggle with handling entire code repositories.

By Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Shieh, Wenmeng Zhou

llms agents benchmarks

arXiv AI

Jul 13

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.

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

llms rag agents benchmarks safety

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 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

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