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ORCIDLLM4CODE@ICSE 2026: Rio de Janeiro, Brazil
- Proceedings of the 3rd International Workshop on Large Language Models For Code, LLM4Code 2026, Rio de JaneiroBrazil, April 12-18, 2026. ACM 2026, ISBN 979-8-4007-2412-1
- Divyanshu Saxena, Rishikesh Maurya, Xiaoxuan Ou, Gagan Somashekar, Shachee Mishra Gupta, Arun Iyer, Yu Kang, Chetan Bansal, Aditya Akella, Saravan Rajmohan:
Continuous Benchmark Generation for Evaluating Enterprise-scale LLM Agents. 1-5 - Stefan Szeider:
CP-Agent: Agentic Constraint Programming. 6-13 - Mahmoud Kassem, Francisco Ribeiro, Sarah Nadi:
An Automated Methodology for Generating Labeled Datasets of Semantic Errors in Code. 14-20 - Shijia Dong, Haoruo Zhao, Paul Harvey:
Code vs Serialized AST Inputs for LLM-Based Code Summarization: An Empirical Study. 21-28 - Mingzhi Zhu, Michele Merler, Boris Sobolev, Rahul Krishna, Raju Pavuluri, Stacy Patterson:
Multi-task Code LLMs: Data Mix or Model Merge? 29-36 - Yijia Tang, Zhiqiu Huang, Jian Xie, Yaoshen Yu, Bowei Xia, Enya Shen, Yukun Cao:
English or Chinese? Investigating the Impact of Prompt Language on Large Language Models for Code Summarization. 37-41 - Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky, Mohamed Amine Ferrag, Lajos Balkanyi, Tamás Bisztray:
The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution. 42-50 - Siu Wun Cheung, Harshitha Menon:
Learning Functional Equivalence via Supervised Contrastive Code-Problem Alignment. 51-58 - Andrei Paleyes, Diana Robinson, Radzim Sendyka, Christian Cabrera, Neil D. Lawrence:
Code Roulette: How Prompt Variability Affects LLM Code Generation. 59-66 - Yihan Dai, Dimitrios Stamatios Bouras, Haoxiang Jia, Sergey Mechtaev:
Statistical Independence Aware Caching for LLM Workflows. 67-72 - Sophie Weidmann, Fernando Castor:
An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code. 73-81 - Antimo Di Bernardo, Gianluca Capozzi, Pasquale De Rosa, Daniele Cono D'Elia, Leonardo Querzoni, Giuseppe Antonio Di Luna, Valerio Schiavoni:
Do LLMs Dream of Energy-Efficient Code? 82-89 - Maxim Tabachnyk, Xu Shu, Alexander Frömmgen, Pavel Sychev, Vahid Meimand, Ilia Krets, Stanislav Pyatykh, Abner Araujo, Kristof Molnar, Satish Chandra:
Achieving Productivity Gains with AI-based IDE features: A Journey at Google. 90-96 - Michele Merler, Rangeet Pan, Rahul Krishna, Tin Kam Ho, Raju Pavuluri, Maja Vukovic:
Usage, Effects and Requirements for AI Coding Assistants in the Enterprise: An Empirical Study. 97-104 - Lucas Aguiar, Matheus Freitas, Matheus Paixão, Rafael Carmo:
Benchmarking LLM Commit Message Generation through a Developer-centric Pairwise Preference Framework. 105-112 - Ferida Mohammed, Fatma Ayad, Petros Maniatis, Satish Chandra, Elizabeth Dinella:
RuberDuckBench: A Benchmark for AI Coding Assistants. 113-120 - Qingzhao Zhang, Z. Morley Mao:
Towards LLM-guided Semantic Validation of Autonomous Driving Safety Policies. 121-125 - Javel Freitas, Guilherme Pereira, Lara Lima, Caio Rian de Sousa, Edivar Filho, José Cezar de Souza Filho, Paulo Henrique M. Maia, Carla I. M. Bezerra:
Evaluating LLMs-Driven Java Code Refactoring from a Developer's Perspective. 126-130 - Jefferson de Barros Santos:
A Spec-Driven Workflow for AI-Assisted Domain-Driven Development: Insights from Practice. 131-134 - Batuhan Raif Karagöz, Mahesh Jayasankar, Saurabh Bodhe, Subhayan Roy, Lejin Varghese, Yonas Bedasso, Max Kiehn:
Towards Improving in-IDE Code Completion for Driver Development. 135-142 - Antony Seabra de Medeiros, Claudio Cavalcante, Nicolaas Ruberg, Sérgio Lifschitz:
LLM-Driven SQL Remediation: Towards Safe and Explainable Code for Automated Schema Refactoring. 143-150 - Nathan Rutherford, Dan O'Keeffe:
An Empirical Study of C to Rust Translation using Local Large-Language Models. 151-158 - Chihao Shen, Connor Dilgren, Purva Chiniya, Luke Griffith, Yu Ding, Yizheng Chen:
SecRepoBench: Benchmarking Code Agents for Secure Code Completion in Real-World Repositories. 159-166 - Duong Pham Duc, Hiroshi Sato, Masao Kubo:
MAsFL: Data-Secure, Efficient and Accurate Fault Localization with Multi-Agent Small Language Models. 167-174 - Arastoo Zibaeirad, Marco Vieira:
Diverse LLMs vs. Vulnerabilities: Who Detects and Fixes Them Better? 175-183 - Edvin Nordqvist, Changjie Wang, Simone Ferlin, Mariano Scazzariello, Marco Chiesa:
RAG Against the Machine: Zero-Shot Software Vulnerabilities Classification using LLMs. 184-191 - Chang Liu:
LLM-Powered On-Demand Test Suites in Self-Graded Student Programming Assignments. 192-196 - Amirkia Rafiei Oskooei, Selcan Yukcu, Mehmet Cevheri Bozoglan, Mehmet S. Aktas:
Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures. 197-205
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