On ChatGPT: perspectives from software engineering students

Hanifi Rüstem, Khadija and Çetin, Orçun and Yılmaz, Cemal (2023) On ChatGPT: perspectives from software engineering students. In: IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS), Chiang Mai, Thailand

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Abstract

ChatGPT, an increasingly popular Large Language Model (LLM), has found widespread acceptance, especially among the younger generation, who rely on it for various tasks, such as comprehending complex course materials and tackling homework assignments. This surge in interest has drawn the attention of researchers, leading to numerous studies that delve into the advantages and disadvantages of the upcoming LLM dominant era. In our research, we explore the influence of ChatGPT and similar models on the field of software engineering, specifically from the perspective of software engineering students. Our main objective is to gain valuable insights into their usage habits and opinions through a comprehensive survey. The survey encompassed diverse questions, addressing the specific areas where ChatGPT was utilized for assistance and gathering students' reflections on each aspect. We found that ChatGPT has garnered widespread acceptance among software engineering students, with 93% of them utilizing it for their projects. These students expressed satisfaction with the level of assistance provided, and most intend to continue using it as a valuable tool in their work. During our investigation, we also assessed the students' awareness of the underlying technologies behind ChatGPT. Approximately half of the students demonstrated awareness of these technologies, while 38.7% had made extra efforts to explore prompt engineering to enhance ChatGPT's productivity. However, an important finding was that 90.6% of the students reported experiencing hallucinations during their interactions with ChatGPT. These hallucinations were shared as examples, raising significant concerns that warrant further exploration and mitigation. Moreover, we delved into potential improvements and gathered valuable recommendations, which could help ChatGPT to become even more effective and dependable in its applications.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: academic education; ChatGPT; generative AI; Large Language Models; software engineering
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Orçun Çetin
Date Deposited: 11 Jun 2024 11:24
Last Modified: 11 Jun 2024 11:24
URI: https://research.sabanciuniv.edu/id/eprint/49050

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