Home / Current Issue / Paper 1724172
A Survey-Based Study on the Adoption of Generative AI in Software Development: Opportunities, Challenges, and User Perspectives
Subject area: Science,Engineering and Technology · Area of research: AI in Software Development
DOI: 10.64388/IREV10I4-1724172
Abstract
Generative AI (GenAI) is increasingly used in software development for coding, debugging, documentation, and learning. This study examines its adoption, benefits, challenges, security concerns, dependence, learning support, and future impact. Survey responses from 21 programming students and professionals were analyzed using frequencies, percentages, and average ratings. Of the respondents, 95.2% reported daily or frequent use and 95.2% used ChatGPT. Debugging and error fixing was the most common task (71.4%). Respondents reported benefits such as better problem-solving and faster development, but also identified bugs, security issues, architectural complexity, and privacy concerns. Overall, GenAI is viewed as a useful assistant, but human review, testing, and responsible use remain essential. The findings are exploratory due to the small sample size.
Keywords
generative AI; software development; ChatGPT; AI-assisted programming; code generation; user perspectives
References
[1] Barke S, James MB, Polikarpova N (2023). Grounded Copilot: How programmers interact with code-generating models. Proceedings of the ACM on Programming Languages, 7(OOPSLA1), 85–111. https://doi.org/10.1145/3586030
[2] GitHub (2024). GitHub Copilot documentation. GitHub.
[3] Google (2024). Gemini documentation. Google.
[4] Noy S, Zhang W (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
[5] OpenAI (2023). GPT-4 technical report. arXiv:2303.08774.
[6] Pearce H, Ahmad B, Tan B, Dolan-Gavitt B, Karri R (2022). Asleep at the keyboard? Assessing the security of GitHub Copilot’s code contributions. In: 2022 IEEE Symposium on Security and Privacy (SP), 754–768. https://doi.org/10.1109/SP46214.2022.9833571
[7] Peng S, Kalliamvakou E, Cihon P, Demirer M (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv:2302.06590.
[8] Vaithilingam P, Zhang T, Glassman EL (2022). Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1–7. https://doi.org/10.1145/3491101.3519665
How to cite this paper
@article{1724172,
author = {Rutuja Rajendra Yadav, Rushikesh Ganesh Yadav, Pratiksha Kamble},
title = { A Survey-Based Study on the Adoption of Generative AI in Software Development: Opportunities, Challenges, and User Perspectives},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {3198-3207},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724172.pdf},
abstract = {Generative AI (GenAI) is increasingly used in software development for coding, debugging, documentation, and learning. This study examines its adoption, benefits, challenges, security concerns, dependence, learning support, and future impact. Survey responses from 21 programming students and professionals were analyzed using frequencies, percentages, and average ratings. Of the respondents, 95.2% reported daily or frequent use and 95.2% used ChatGPT. Debugging and error fixing was the most common task (71.4%). Respondents reported benefits such as better problem-solving and faster development, but also identified bugs, security issues, architectural complexity, and privacy concerns. Overall, GenAI is viewed as a useful assistant, but human review, testing, and responsible use remain essential. The findings are exploratory due to the small sample size.},
keywords = {generative AI; software development; ChatGPT; AI-assisted programming; code generation; user perspectives},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724172}
}