LLM4Code 2024 Contributors

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Results (114)

A Loubna Ben AllalAllal, Loubna Ben Hugging Face Open development of Large Language Models for code with BigCode and StarCoder2
Carolyn Jane AndersonAnderson, Carolyn Jane Wellesley CollegeUnited States Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code
Gabor AntalAntal, Gabor University of Szeged Toward a New Era of Rapid Development: Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation
Virendra AshiwalAshiwal, Virendra ABB Research LLM-based and Retrieval-Augmented Control Code Generation
B Hannah McLean BabeBabe, Hannah McLean Oberlin College StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code
Shreya BhatiaBhatia, Shreya IIIT Delhi Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools
Abby Brennan-JonesBrennan-Jones, Abby Wellesley College Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
Patrick BrophyBrophy, Patrick Univeristy of Wisconsin-Madison PromptSet: A Programmer’s Prompting Dataset
Nghi D. Q. BuiBui, Nghi D. Q. Fulbright University, Viet Nam HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations
C Yushi CaoCao, Yushi Nanyang Technological University LLMs for Relational Reasoning: How Far are We?
Federico CassanoCassano, Federico Northeastern University Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
Wei ChenChen, Wei Beijing Jiaotong University Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMs
Cheng ChengCheng, Cheng Concordia University Benchmarking the Security Aspect of Large Language Model-Based Code Generation
Bhavya ChopraChopra, Bhavya MicrosoftIndia Semantically Aligned Question and Code Generation for Automated Insight Generation
Krerkkiat ChusapChusap, Krerkkiat Ohio UniversityUnited States Gauging Tech Community Acceptance of Rapid Prototyping in Unfamiliar Programming Languages using LLM Chatbots
D George DasoulasDasoulas, George Harvard University Learn to Code Sustainably: An Empirical Study on Green Code Generation
Ippolyti DellatolasDellatolas, Ippolyti Massachusetts Institute of Technology Learn to Code Sustainably: An Empirical Study on Green Code Generation
Prem DevanbuDevanbu, Prem University of California at DavisUnited States Session Chair of Session 4: Full Papers + Award & Closing (part of LLM4Code) Welcome & Opening
Adam DingleDingle, Adam Charles University Tackling Students' Coding Assignments with LLMs
E Nafise EskandaniEskandani, Nafise ABB Corporate Research Center LLM-based and Retrieval-Augmented Control Code Generation
F Haoxiang FeiFei, Haoxiang International Digital Economy Academy MoonBit: Explore the Design of an AI-Friendly Programming Language
Molly FeldmanFeldman, Molly Oberlin CollegeUnited States StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code
Rudolf FerencFerenc, Rudolf University of SzegedHungary Toward a New Era of Rapid Development: Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation
Lukas FischerFischer, Lukas Software Competence Center Hagenberg GmbHAustria Industrial Experience Report on AI-Assisted Coding in Professional Software Development
G Shubham GandhiGandhi, Shubham TCS ResearchIndia Translation of Low-Resource COBOL to Logically Correct and Readable Java leveraging High-Resource Java Refinement
Tarushi GandhiGandhi, Tarushi IIIT Delhi Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools
Skyler GrandelGrandel, Skyler Vanderbilt UniversityUnited States Applying Large Language Models to Enhance the Assessment of Parallel Functional Programming Assignments
Sten GrünerGrüner, Sten ABB Corporate ResearchGermany LLM-based and Retrieval-Augmented Control Code Generation
Wang GuanlinGuanlin, Wang National University of Singapore An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project
Arjun GuhaGuha, Arjun Northeastern University; RobloxUnited States Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code
Sumit GulwaniGulwani, Sumit MicrosoftUnited States Semantically Aligned Question and Code Generation for Automated Insight Generation
H Rhaban HarkHark, Rhaban ABB Research LLM-based and Retrieval-Augmented Control Code Generation
Rene HeinzlHeinzl, Rene Building Digital Solutions 421 GmbH Industrial Experience Report on AI-Assisted Coding in Professional Software Development
Austin HenleyHenley, Austin University of TennesseeUnited States Semantically Aligned Question and Code Generation for Automated Insight Generation
Tim HoffmannHoffmann, Tim Mercedes-Benz Learn to Code Sustainably: An Empirical Study on Green Code Generation
Yintong HuoHuo, Yintong The Chinese University of Hong KongHong Kong SAR China Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context
I Ganesh Neelakanta IyerIyer, Ganesh Neelakanta National University of Singapore An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project
J Pankaj JaloteJalote, Pankaj IIIT Delhi Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools
Junzhe JiangJiang, Junzhe Hong Kong Polytechnic University LLMs for Relational Reasoning: How Far are We?
Shengbei JiangJiang, Shengbei Beijing Jiaotong University Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMs
Ishan JoshiJoshi, Ishan Univeristy of Wisconsin-Madison PromptSet: A Programmer’s Prompting Dataset
K Michael KeckeisenKeckeisen, Michael TWT Science and Innovation Learn to Code Sustainably: An Empirical Study on Green Code Generation
Jyotsana KhatriKhatri, Jyotsana TCS Research Translation of Low-Resource COBOL to Logically Correct and Readable Java leveraging High-Resource Java Refinement
Anirudh KhatryKhatry, Anirudh Microsoft Semantically Aligned Question and Code Generation for Automated Insight Generation
Sotirios KotsopoulosKotsopoulos, Sotirios National Technical University of Athens, Massachusetts Institute of Technology Learn to Code Sustainably: An Empirical Study on Green Code Generation
Anne KoziolekKoziolek, Anne Karlsruhe Institute of TechnologyGermany LLM-based Control Code Generation using Image Recognition
Heiko KoziolekKoziolek, Heiko ABB Corporate ResearchGermany LLM-based and Retrieval-Augmented Control Code Generation LLM-based Control Code Generation using Image Recognition
Martin KrulišKruliš, Martin Charles UniversityCzechia Tackling Students' Coding Assignments with LLMs
Dhruv KumarKumar, Dhruv Indraprastha Institute of Information Technology, DelhiIndia Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools
L Vu LeLe, Vu MicrosoftUnited States Semantically Aligned Question and Code Generation for Automated Insight Generation
Kevin LeachLeach, Kevin Vanderbilt UniversityUnited States Applying Large Language Models to Enhance the Assessment of Parallel Functional Programming Assignments
Tao LiLi, Tao Northeastern University Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
Yichen LILI, Yichen The Chinese University of Hong KongChina Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context
Zhiming LiLi, Zhiming Nanyang Technological University, SingaporeChina LLMs for Relational Reasoning: How Far are We?
Shang-Wei LinLin, Shang-Wei Nanyang Technological UniversitySingapore LLMs for Relational Reasoning: How Far are We?
Sofia LinsbauerLinsbauer, Sofia ABB Research LLM-based and Retrieval-Augmented Control Code Generation
Chang LiuLiu, Chang Gauging Tech Community Acceptance of Rapid Prototyping in Unfamiliar Programming Languages using LLM Chatbots
Qing LiuLiu, Qing International Digital Economy Academy MoonBit: Explore the Design of an AI-Friendly Programming Language
Xu LiuLiu, Xu North Carolina State UniversityUnited States LLMs for Relational Reasoning: How Far are We?
Yang LiuLiu, Yang Nanyang Technological UniversitySingapore LLMs for Relational Reasoning: How Far are We?
Michael LonesLones, Michael Heriot Watt University- UK Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student Programmers
Yiling LouLou, Yiling Fudan UniversityChina Session Chair of Session 2: Full Papers (part of LLM4Code)
Yiling LouLou, Yiling Fudan UniversityChina Welcome & Opening
Anton LozhkovLozhkov, Anton Hugging Face Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
Michael LyuLyu, Michael The Chinese University of Hong Kong Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context
M Jie M. ZhangM. Zhang, Jie King's College LondonUnited Kingdom Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMs
Manuel MaarekMaarek, Manuel Heriot-Watt UniversityUnited Kingdom Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student Programmers
Raveendra Kumar MedicherlaMedicherla, Raveendra Kumar TCS Research, Tata Consultancy ServicesIndia Translation of Low-Resource COBOL to Logically Correct and Readable Java leveraging High-Resource Java Refinement
Michael MoserMoser, Michael Software Competence Center Hagenberg GmbHAustria Industrial Experience Report on AI-Assisted Coding in Professional Software Development
N Sydney NguyenNguyen, Sydney Wellesley College StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code
Thai Minh NguyenNguyen, Thai Minh Monash University HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations
Markus NisslNissl, Markus Building Digital Solutions 421 GmbH Industrial Experience Report on AI-Assisted Coding in Professional Software Development
P Chris ParninParnin, Chris Microsoft Semantically Aligned Question and Code Generation for Automated Insight Generation
Manasi PatwardhanPatwardhan, Manasi TCS Research Translation of Low-Resource COBOL to Logically Correct and Readable Java leveraging High-Resource Java Refinement
Dhruba Jyoti PaulPaul, Dhruba Jyoti Univeristy of Wisconsin-Madison PromptSet: A Programmer’s Prompting Dataset
Yun PengPeng, Yun The Chinese University of Hong KongChina Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context
Kaiser PisterPister, Kaiser Univeristy of Wisconsin-Madison PromptSet: A Programmer’s Prompting Dataset
Sanyogita PiyaPiya, Sanyogita The University of Texas at Arlington LLM4TDD: Best Practices for Test Driven Development Using Large Language Models
R Rudolf RamlerRamler, Rudolf Software Competence Center Hagenberg (SCCH)Austria Industrial Experience Report on AI-Assisted Coding in Professional Software Development
Dezhi RanRan, Dezhi Peking UniversityChina HawkEyes: Spotting and Evading Instruction Disalignments of LLMs
Sanka RasnayakaRasnayaka, Sanka National University of Singapore An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project
Baptiste RozièreRozière, Baptiste Meta Code Llama: Open Foundation Models for Code
S Smitha S KumarS Kumar, Smitha Heriot-Watt University -UAE Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student Programmers
Douglas C. SchmidtSchmidt, Douglas C. Vanderbilt University Applying Large Language Models to Enhance the Assessment of Parallel Functional Programming Assignments
Maximilian SchmidtSchmidt, Maximilian TWT Science and Innovation Learn to Code Sustainably: An Empirical Study on Green Code Generation
Florian SchneiderSchneider, Florian TWT Science and Innovation Learn to Code Sustainably: An Empirical Study on Green Code Generation
Akul SethiSethi, Akul Northeastern UniversityUnited States Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
Ridwan Salihin ShariffdeenShariffdeen, Ridwan Salihin National University of SingaporeSingapore An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project
Noah ShinnShinn, Noah Northeastern University Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
Mukul SinghSingh, Mukul Microsoft Semantically Aligned Question and Code Generation for Automated Insight Generation
Ananya SinghaSingha, Ananya Microsoft Semantically Aligned Question and Code Generation for Automated Insight Generation
Zihe SongSong, Zihe University of Texas at DallasUnited States HawkEyes: Spotting and Evading Instruction Disalignments of LLMs
Allison SullivanSullivan, Allison University of Texas at ArlingtonUnited States LLM4TDD: Best Practices for Test Driven Development Using Large Language Models
T Lin TanTan, Lin Purdue UniversityUnited States Session Chair of Session 1: Welcome & Opening + Keynote 1 + Full Papers (part of LLM4Code) Welcome & Opening
Yon Shin TeoTeo, Yon Shin Continental Automotive Singapore Pte. Ltd. LLMs for Relational Reasoning: How Far are We?
V Tina VartziotisVartziotis, Tina TWT Science and Innovation, National Technical University of AthensGermany Learn to Code Sustainably: An Empirical Study on Green Code Generation
Gust VerbruggenVerbruggen, Gust Microsoft Semantically Aligned Question and Code Generation for Automated Insight Generation
Lovekesh VigVig, Lovekesh TCS Research, New Delhi, India Translation of Low-Resource COBOL to Logically Correct and Readable Java leveraging High-Resource Java Refinement
Richárd VozárVozár, Richárd Department of Software Engineering, University of Szeged, Hungary Toward a New Era of Rapid Development: Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation
W Bo WangWang, Bo Beijing Jiaotong UniversityChina Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMs
Yanlin WangWang, Yanlin Sun Yat-sen UniversityChina MoonBit: Explore the Design of an AI-Friendly Programming Language
X Tao XieXie, Tao Peking UniversityChina HawkEyes: Spotting and Evading Instruction Disalignments of LLMs
Xiufeng XuXu, Xiufeng Nanyang Technological University LLMs for Relational Reasoning: How Far are We?
Y Jinqiu YangYang, Jinqiu Concordia UniversityCanada Benchmarking the Security Aspect of Large Language Model-Based Code Generation
Wei YangYang, Wei University of Texas at DallasUnited States HawkEyes: Spotting and Evading Instruction Disalignments of LLMs
Z Hind ZantoutZantout, Hind Heriot-Watt University -UAE Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student Programmers
Hongbo ZhangZhang, Hongbo International Digital Economy Academy MoonBit: Explore the Design of an AI-Friendly Programming Language
Jiabao ZhangZhang, Jiabao Beijing Jiaotong University Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMs
Lingming ZhangZhang, Lingming University of Illinois at Urbana-ChampaignUnited States Session Chair of Session 3: Keynote 2 + Position Papers (part of LLM4Code)
Lingming ZhangZhang, Lingming University of Illinois at Urbana-Champaign Welcome & Opening
Wenhan ZhangZhang, Wenhan Peking University HawkEyes: Spotting and Evading Instruction Disalignments of LLMs
Yu ZhangZhang, Yu International Digital Economy Academy MoonBit: Explore the Design of an AI-Friendly Programming Language
Jianyi ZhouZhou, Jianyi Huawei Cloud Computing Technologies Co., Ltd. Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMs
Yangtian ZiZi, Yangtian Northeastern University StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code