Inappropriate Benefits and Identification of ChatGPT Misuse in Programming Tests: A Controlled Experiment

• URL: http://arxiv.org/abs/2309.16697v1
• Date: Fri, 11 Aug 2023 06:42:29 GMT
• Title: Inappropriate Benefits and Identification of ChatGPT Misuse in Programming Tests: A Controlled Experiment
• Authors: Hapnes Toba, Oscar Karnalim, Meliana Christianti Johan, Terutoshi Tada, Yenni Merlin Djajalaksana, Tristan Vivaldy
• Abstract summary: Students can ask ChatGPT to complete a programming task, generating a solution from other people's work without proper acknowledgment of the source(s) We performed a controlled experiment measuring the inappropriate benefits of using ChatGPT in terms of completion time and programming performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While ChatGPT may help students to learn to program, it can be misused to do plagiarism, a breach of academic integrity. Students can ask ChatGPT to complete a programming task, generating a solution from other people's work without proper acknowledgment of the source(s). To help address this new kind of plagiarism, we performed a controlled experiment measuring the inappropriate benefits of using ChatGPT in terms of completion time and programming performance. We also reported how to manually identify programs aided with ChatGPT (via student behavior while using ChatGPT) and student perspective of ChatGPT (via a survey). Seventeen students participated in the experiment. They were asked to complete two programming tests. They were divided into two groups per the test: one group should complete the test without help while the other group should complete it with ChatGPT. Our study shows that students with ChatGPT complete programming tests two times faster than those without ChatGPT, though their programming performance is comparable. The generated code is highly efficient and uses complex data structures like lists and dictionaries. Based on the survey results, ChatGPT is recommended to be used as an assistant to complete programming tasks and other general assignments. ChatGPT will be beneficial as a reference as other search engines do. Logical and critical thinking are needed to validate the result presented by ChatGPT.

Related papers

• ChatGPT and Human Synergy in Black-Box Testing: A Comparative Analysis [0.0]
  ChatGPT can generate test cases that generally match or slightly surpass those created by human participants. When ChatGPT cooperates with humans, it can cover considerably more test viewpoints than each can achieve alone.
  arXiv  Detail & Related papers  (2024-01-25T03:42:17Z)
• Integrating ChatGPT in a Computer Science Course: Students Perceptions and Suggestions [0.0]
  This experience report explores students' perceptions and suggestions for integrating ChatGPT in a computer science course. Findings show the importance of carefully balancing using ChatGPT in computer science courses.
  arXiv  Detail & Related papers  (2023-12-22T10:48:34Z)
• Can ChatGPT Play the Role of a Teaching Assistant in an Introductory Programming Course? [1.8197265299982013]
  This paper explores the potential of using ChatGPT, an LLM, as a virtual Teaching Assistant (TA) in an introductory programming course. We evaluate ChatGPT's capabilities by comparing its performance with that of human TAs in some of the important TA functions.
  arXiv  Detail & Related papers  (2023-12-12T15:06:44Z)
• Exploring ChatGPT's Capabilities on Vulnerability Management [56.4403395100589]
  We explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 70,346 samples. One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports. Our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions.
  arXiv  Detail & Related papers  (2023-11-11T11:01:13Z)
• Primacy Effect of ChatGPT [69.49920102917598]
  We study the primacy effect of ChatGPT: the tendency of selecting the labels at earlier positions as the answer. We hope that our experiments and analyses provide additional insights into building more reliable ChatGPT-based solutions.
  arXiv  Detail & Related papers  (2023-10-20T00:37:28Z)
• Chatbot-supported Thesis Writing: An Autoethnographic Report [0.0]
  ChatGPT might be applied to formats that require learners to generate text, such as bachelor theses or student research papers. ChatGPT is to be valued as a beneficial tool in thesis writing. However, writing a conclusive thesis still requires the learner's meaningful engagement.
  arXiv  Detail & Related papers  (2023-10-14T09:09:26Z)
• ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks [2.084078990567849]
  ChatGPT (Chat Generative Pre-trained Transformer) launched by OpenAI on November 30, 2022. In this study, we explore how ChatGPT can be used to help with common software engineering tasks.
  arXiv  Detail & Related papers  (2023-05-26T11:29:06Z)
• ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time [54.18651663847874]
  ChatGPT has achieved great success and can be considered to have acquired an infrastructural status. Existing benchmarks encounter two challenges: (1) Disregard for periodical evaluation and (2) Lack of fine-grained features. We construct ChatLog, an ever-updating dataset with large-scale records of diverse long-form ChatGPT responses for 21 NLP benchmarks from March, 2023 to now.
  arXiv  Detail & Related papers  (2023-04-27T11:33:48Z)
• Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT [103.57103957631067]
  ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. We evaluate ChatGPT's understanding ability by evaluating it on the most popular GLUE benchmark, and comparing it with 4 representative fine-tuned BERT-style models. We find that: 1) ChatGPT falls short in handling paraphrase and similarity tasks; 2) ChatGPT outperforms all BERT models on inference tasks by a large margin; 3) ChatGPT achieves comparable performance compared with BERT on sentiment analysis and question answering tasks.
  arXiv  Detail & Related papers  (2023-02-19T12:29:33Z)
• A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity [79.12003701981092]
  We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. ChatGPT is 63.41% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning.
  arXiv  Detail & Related papers  (2023-02-08T12:35:34Z)
• Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [113.22611481694825]
  Large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community. It is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot.
  arXiv  Detail & Related papers  (2023-02-08T09:44:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.