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ChatGPT as a Software Development Bot: A Project-based Study

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arxiv 2310.13648 v2 pith:HJQKCLYS submitted 2023-10-20 cs.SE

Muhammad Waseem , Teerath Das , Aakash Ahmad , Peng Liang , Mahdi Fehmideh , Tommi Mikkonen This is my paper · ORCID
classification cs.SE
keywords chatgptdevelopmentsoftwarelearningaccuracycollaborationeducationgaps
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Artificial Intelligence has demonstrated its significance in software engineering through notable improvements in productivity, accuracy, collaboration, and learning outcomes. This study examines the impact of generative AI tools, specifically ChatGPT, on the software development experiences of undergraduate students. Over a three-month project with seven students, ChatGPT was used as a support tool. The research focused on assessing ChatGPT's effectiveness, benefits, limitations, and its influence on learning. Results showed that ChatGPT significantly addresses skill gaps in software development education, enhancing efficiency, accuracy, and collaboration. It also improved participants' fundamental understanding and soft skills. The study highlights the importance of incorporating AI tools like ChatGPT in education to bridge skill gaps and increase productivity, but stresses the need for a balanced approach to technology use. Future research should focus on optimizing ChatGPT's application in various development contexts to maximize learning and address specific challenges.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Impact of AI-Generated Solutions on Software Architecture and Productivity: Results from a Survey Study

    cs.SE 2025-06 reject novelty 4.0 of 10

    In a 40-person practitioner survey, AI tools were reported to help most with small code snippets and to degrade architecture quality when applied to large, complex problems.

  2. Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3

    cs.SE 2025-04 reject novelty 4.0 of 10

    On a small synthetic four-language dataset, GPT-4.0 detects code smells with higher precision than DeepSeek-V3, while both models miss most annotated smells and the cost comparison is unreliable.

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