Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models
This program is tentative and subject to change.
Large language models (LLMs) have brought a paradigm shift to the field of code generation, offering the potential to enhance the software development process. However, previous research mainly focuses on the accuracy of code generation, while coding style differences between LLMs and human developers remain under-explored. In this paper, we empirically analyze the differences in coding style between the code generated by mainstream Code LLMs and the code written by human developers, and summarize coding style inconsistency taxonomy. Specifically, we first summarize the types of coding style inconsistencies by manually analyzing a large number of generation results. We then compare the code generated by Code LLMs with the code written by human programmers in terms of readability, conciseness, and robustness. The results reveal that LLMs and developers have different coding styles. Additionally, we study the possible causes of these inconsistencies and provide some solutions to alleviate the problem.
This program is tentative and subject to change.
Tue 24 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:30 - 12:30 | |||
10:30 20mTalk | An Empirical Study of the Non-determinism of ChatGPT in Code Generation Journal First Shuyin Ouyang King's College London, Jie M. Zhang King's College London, Mark Harman Meta Platforms, Inc. and UCL, Meng Wang University of Bristol | ||
10:50 20mTalk | Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues Journal First Yue Liu Monash University, Le-Cong Thanh The University of Melbourne, Ratnadira Widyasari Singapore Management University, Singapore, Kla Tantithamthavorn Monash University, Li Li Beihang University, Xuan-Bach D. Le University of Melbourne, David Lo Singapore Management University | ||
11:10 20mTalk | Divide-and-Conquer: Generating UI Code from Screenshots Research Papers YUXUAN WAN , Chaozheng Wang The Chinese University of Hong Kong, Yi Dong The Chinese University of Hong Kong, Wenxuan Wang Chinese University of Hong Kong, Shuqing Li The Chinese University of Hong Kong, Yintong Huo Singapore Management University, Michael Lyu Chinese University of Hong Kong | ||
11:30 20mTalk | LLM-based Method Name Suggestion with Automatically Generated Context-Rich Prompts Research Papers Waseem Akram Beijing Institute of Technology, Yanjie Jiang Peking University, Yuxia Zhang Beijing Institute of Technology, Haris Ali Khan Beijing Institute of Technology, Hui Liu Beijing Institute of Technology | ||
11:50 20mTalk | Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models Research Papers Yanlin Wang Sun Yat-sen University, Tianyue Jiang Sun Yat-sen University, Mingwei Liu Sun Yat-Sen University, Jiachi Chen Sun Yat-sen University, Mingzhi Mao Sun Yat-sen University, Xilin Liu Huawei Cloud, Yuchi Ma Huawei Cloud Computing Technologies, Zibin Zheng Sun Yat-sen University | ||
12:10 20mTalk | Don’t Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems Journal First Zhensu Sun Singapore Management University, Xiaoning Du Monash University, Fu Song Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences; Nanjing Institute of Software Technology, Shangwen Wang National University of Defense Technology, Mingze Ni University of Technology Sydney, Li Li Beihang University, David Lo Singapore Management University | ||
This is the main event hall of Clarion Hotel, which will be used to host Keynotes and other plenary sessions. The FSE banquet will also happen in this room.
Just in front of the registration desk, on the other side of the main conference area, you find two large doors to enter the Cosmos Hall.