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ORCIDLLM4CODE@ICSE 2024: Lisbon, Portugal
- Proceedings of the 1st International Workshop on Large Language Models for Code, LLM4Code 2024, Lisbon, Portugal, 20 April 2024. ACM 2024, ISBN 979-8-4007-0579-3 [contents]
- Rudolf Ramler
, Michael Moser, Lukas Fischer, Markus Nissl, René Heinzl:
Industrial Experience Report on AI-Assisted Coding in Professional Software Development. 1-7 - Krerkkiat Chusap, Chang Liu:
Gauging Tech Community Acceptance of Rapid Prototyping in Unfamiliar Programming Languages using LLM Chatbots. 8-13 - Sanyogita Piya, Allison Sullivan:
LLM4TDD: Best Practices for Test Driven Development Using Large Language Models. 14-21 - Heiko Koziolek, Sten Grüner, Rhaban Hark, Virendra Ashiwal, Sofia Linsbauer, Nafise Eskandani:
LLM-based and Retrieval-Augmented Control Code Generation. 22-29 - Tina Vartziotis, Ippolyti Dellatolas, George Dasoulas, Maximilian Schmidt, Florian Schneider, Tim Hoffmann, Sotirios Kotsopoulos, Michael Keckeisen:
Learn to Code Sustainably: An Empirical Study on Green Code Generation. 30-37 - Heiko Koziolek, Anne Koziolek:
LLM-based Control Code Generation using Image Recognition. 38-45 - Shubham Gandhi, Manasi Patwardhan, Jyotsana Khatri, Lovekesh Vig, Raveendra Kumar Medicherla:
Translation of Low-Resource COBOL to Logically Correct and Readable Java leveraging High-Resource Java Refinement. 46-53 - Shreya Bhatia, Tarushi Gandhi, Dhruv Kumar, Pankaj Jalote:
Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools. 54-61 - Kaiser Pister, Dhruba Jyoti Paul, Ishan Joshi, Patrick Brophy:
PromptSet: A Programmer's Prompting Dataset. 62-69 - Yichen Li, Yun Peng, Yintong Huo, Michael R. Lyu:
Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context. 70-74 - Shengbei Jiang, Jiabao Zhang, Wei Chen, Bo Wang, Jianyi Zhou, Jie Zhang:
Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMs. 75-78 - Haoxiang Fei, Yu Zhang, Hongbo Zhang, Yanlin Wang, Qing Liu:
MoonBit: Explore the Design of an AI-Friendly Programming Language. 79-83 - Gábor Antal, Richárd Vozár, Rudolf Ferenc:
Toward a New Era of Rapid Development: Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation. 84-87 - Smitha S. Kumar, Michael Adam Lones, Manuel Maarek, Hind Zantout:
Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student Programmers. 88-93 - Adam Dingle, Martin Krulis:
Tackling Students' Coding Assignments with LLMs. 94-101 - Skyler Grandel, Douglas C. Schmidt, Kevin Leach:
Applying Large Language Models to Enhance the Assessment of Parallel Functional Programming Assignments. 102-110 - Sanka Rasnayaka, Guanlin Wang, Ridwan Shariffdeen, Ganesh Neelakanta Iyer:
An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project. 111-118 - Zhiming Li, Yushi Cao, Xiufeng Xu, Junzhe Jiang, Xu Liu, Yon Shin Teo, Shang-Wei Lin, Yang Liu:
LLMs for Relational Reasoning: How Far are We? 119-126 - Ananya Singha, Bhavya Chopra, Anirudh Khatry, Sumit Gulwani, Austin Z. Henley, Vu Le, Chris Parnin, Mukul Singh, Gust Verbruggen:
Semantically Aligned Question and Code Generation for Automated Insight Generation. 127-134
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