default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDFORGE@ICSE 2026: Rio de Janeiro, Brazil
- Proceedings of the 2026 IEEE/ACM Third International Conference on AI Foundation Models and Software Engineering, FORGE 2026, Rio de Janeiro, Brazil, April 12-13, 2026. ACM 2026, ISBN 979-8-4007-2477-0
Research Track
- Gabriel Vitor Klaumann Gubert, Stefan Kugele, Munir Georges:
A Hybrid LLM-Guided Approach to Code Migration Using API-Derived Rules. 1-5 - Zhili Zeng, Kimya Khakzad Shahandashti, Alvine Boaye Belle, Song Wang, Zhen Ming (Jack) Jiang:
An Experience Report on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights. 6-16 - Laboni Sarker, Mara Downing, Achintya Desai, Tevfik Bultan:
Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation. 17-28 - Ragib Shahariar Ayon, Shibbir Ahmed:
AutoReSpec: A Framework for Generating Specification using Large Language Models. 29-39 - Pankaj Manoharlal Thakur, Kyle Thomson, Wesley Klewerton Guez Assunção, Bowen Xu:
Backporting in Robot Operating System: Identifying Commit Purpose and Propagation Need with Large Language Models. 40-50 - Markus Borg, Nadim Hagatulah, Adam Tornhill, Emma Söderberg:
Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics. 51-61 - Sai Sanjna Chintakunta, Nathalia Nascimento, Everton Guimarães:
CodeViz: Collaborative Multi-Agent System for Analytical and Visualization Tasks in Data Science. 62-66 - Mert Tiftikci, Amir Molzam Sharifloo, Mira Mezini:
Deep Graph-Language Fusion for Structure-Aware Code Generation. 67-71 - Dipin Khati, Daniel Rodríguez-Cárdenas, Paul Pantzer, Denys Poshyvanyk:
Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis. 72-76 - Houcine Abdelkader Cherief, Florent Avellaneda, Naouel Moha:
DynamicsLLM: a Dynamic Analysis-based Tool for Generating Intelligent Execution Traces Using LLMs to Detect Android Behavioural Code Smells. 77-87 - Saurabh Bodhe, Sanjukta De, Subhayan Roy, Jaydip Pokiya, Indira Vats, Sehajpreet Kaur, Xuemeng Li, Lejin Varghese, Yonas Bedasso, Max Kiehn:
Execution-free Agentic Program Repair for Enterprise-Scale Development. 88-98 - Pablo Valle, Shaukat Ali, Aitor Arrieta:
Exploring the Potential of Large Language Models in Simulink-Stateflow Mutant Generation. 99-109 - Chengyan Ma, Jieke Shi, Ruidong Han, Ye Liu, Yuqing Niu, David Lo:
Finding Missing Input Validation in TEEs via LLM-Assisted Symbolic Execution. 110-115 - Alessandro Midolo, Emiliano Tramontana, Massimiliano Di Penta:
From Human to Machine Refactoring: Assessing GPT-4's Impact on Python Class Quality and Readability. 116-127 - Rafael Tomaz, Paloma Guenes, Allysson Allex Araújo, Maria Teresa Baldassarre, Marcos Kalinowski:
Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study. 128-138 - Songrui Li, Hanmo You, Jiajun Jiang:
Jailbreaking Large Language Models via Multi-Task Embedding-based Prompt. 139-143 - Eduard Andrei Cristea, Petter Molnes, Jingyue Li:
MalCVE: Malware Detection and CVE Association Using Large Language Models. 144-154 - Ye Liu, Ruidong Han, Chengyan Ma, Yuqing Niu, David Lo:
PatchGPT: Multi-Agent Patch Backporting without Model Fine-Tuning. 155-159 - Kerui Huang, Xian Zhan, Xin Xia:
PIChecker: Automatic Privacy Detector for Third Party Libraries in Android Apps. 160-171 - Evelien Riddell, James Riddell, Gengyi Sun, Michal Antkiewicz, Krzysztof Czarnecki:
Stalled, Biased, and Confused: Uncovering Reasoning Failures in LLMs for Cloud-Based Root Cause Analysis. 172-183 - Alexander Korn, Lea Zaruchas, Chetan Arora, Andreas Metzger, Sven Smolka, Fanyu Wang, Andreas Vogelsang:
Reporting LLM Prompting in Automated Software Engineering: A Guideline Based on Current Practices and Expectations. 184-194 - Luis Filipe Fernandes Gomes, Xin Zhou, David Lo, Rui Abreu:
Visual Loop: Bridging the Cognitive Gap in Software Development Through Visual-AI Collaboration. 195-200 - Saumendu Roy, Banani Roy, Chanchal Roy, Richard Bassey:
XMENTOR: A Rank-Aware Aggregation Approach for Human-Centered Explainable AI in Just-in-Time Software Defect Prediction. 201-212
Data and Benchmarking Track
- James Meaden, Markus Borg:
COMPASS: A Psychometrics-Guided Multi-Dimensional Benchmark for Code Generation Evaluation. 213-217 - Matheus Barbosa, Pedro Baptista, João Eduardo Montandon:
MiG.4: A Curated Dataset of Library Migrations in Java and Python. 218-222 - Sebastian Siedler, Karim Elish:
MIRROR: A Dataset of Structural Metrics for Repackaged Android Apps. 223-227 - Jiaxuan Liang, Shide Zhou, Kailong Wang:
OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools. 228-232 - Rafael Araujo Magesty, João Eduardo A Montandon:
PromiseAwait: A Dataset of JavaScript Migrations from Promises to Async/Await. 233-237 - Alexandra González, Xavier Franch, Silverio Martínez-Fernández:
SEMODS: A Validated Dataset of Open-Source Software Engineering Models. 238-242 - Daniel Rodríguez-Cárdenas, Xiaochang Li, Marcos Macedo, Antonio Mastropaolo, Dipin Khati, Yuan Tian, Huajie Shao, Denys Poshyvanyk:
Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering. 243-248 - Cole Granger, Dipin Khati, Daniel Rodríguez-Cárdenas, Denys Poshyvanyk:
Tricky²: Towards a Benchmark for Evaluating Human and LLM Error Interactions. 249-253 - Patrik Drazic, Benaoumeur Senouci, Boualem Benatallah:
VHDL-Instruct: Training Open Dataset for LLMs Benchmarking and HDL Code Generation. 254-258
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2026-07-23 22:08 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated, funded, and maintained by:
Schloss Dagstuhl - Leibniz Center for Informatics is a member of the Leibniz Association and is funded by the federal government of Germany and the states of Germany, especially Saarland and Rhineland‑Palatinate.
