ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal
Wed 17 Apr 2024 12:15 - 12:30 at Luis de Freitas Branco - Generative AI studies Chair(s): Walid Maalej

Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct. In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were obtained from various sources, including 80 from Quescol, 3,264 from Kaggle, and 1,725 from LeetCode. From the dataset, we created 13 sets of code problem variant prompts, which were used to instruct ChatGPT to generate the outputs. Subsequently, we assessed the performance of five AIGC detectors. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.

Wed 17 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Generative AI studiesResearch Track / Software Engineering Education and Training at Luis de Freitas Branco
Chair(s): Walid Maalej University of Hamburg
11:00
15m
Talk
ChatGPT Incorrectness Detection in Software Reviews
Research Track
Minaoar Hossain Tanzil University of Calgary, Canada, Junaed Younus Khan University of Calgary, Gias Uddin York University, Canada
DOI Pre-print
11:15
15m
Talk
ChatGPT-Resistant Screening Instrument for Identifying Non-Programmers
Research Track
Raphael Serafini Ruhr University Bochum, Clemens Otto Ruhr University Bochum, Stefan Albert Horstmann Ruhr University Bochum, Alena Naiakshina Ruhr University Bochum
11:30
15m
Talk
Development in times of hype: How freelancers explore Generative AI?
Research Track
Mateusz Dolata University of Zurich, Norbert Lange Entschleunigung Lange, Gerhard Schwabe University of Zurich
DOI Pre-print
11:45
15m
Talk
How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineering
Research Track
Rudrajit Choudhuri Oregon State University, Dylan Liu Oregon State University, Igor Steinmacher Northern Arizona University, Marco Gerosa Northern Arizona University, Anita Sarma Oregon State University
Pre-print
12:00
15m
Research paper
Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs
Research Track
Mia Mohammad Imran Virginia Commonwealth University, Preetha Chatterjee Drexel University, USA, Kostadin Damevski Virginia Commonwealth University
Pre-print
12:15
15m
Talk
Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education
Software Engineering Education and Training
Wei Hung Pan School of Information Technology, Monash University Malaysia, Ming Jie Chok School of Information Technology, Monash University Malaysia, Jonathan Leong Shan Wong School of Information Technology, Monash University Malaysia, Yung Xin Shin School of Information Technology, Monash University Malaysia, Yeong Shian Poon School of Information Technology, Monash University Malaysia, Zhou Yang Singapore Management University, Chun Yong Chong Monash University Malaysia, David Lo Singapore Management University, Mei Kuan Lim Monash University Malaysia
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