The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Generative Ai in Software Development : an Overview and Evaluation of Modern Coding Tools
Open accessAbstract
Generative AI has significantly transformed software development by leveraging advanced machine learning models to automate coding tasks, generate code, and enhance productivity. This paper provides an overview and evaluation of modern AI-powered coding tools, including GitHub Copilot, OpenAI Codex, DeepCode, Amazon CodeGuru, TabNine, Kite, and IntelliCode, which use large language models (LLMs) to offer real-time code suggestions, automated error detection, and intelligent code completions. Despite their benefits, these tools face challenges related to accuracy, contextual understanding, security, privacy, and ethical considerations, necessitating thorough review and testing of AI-generated code by developers. The integration of AI in coding also raises concerns about proprietary information protection and ethical implications such as job displacement. This paper explores the capabilities, applications, and limitations of current generative AI tools, highlighting their impact on software development and discussing future directions. Emphasis is placed on the need for improved model training, enhanced contextual understanding, secure AI training methods, and ethical AI usage. By addressing these challenges, the industry can maximize the potential of generative AI, creating more accurate, reliable, and ethically sound tools that support a collaborative and innovative software development environment.
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“on the other hand, they increase the risk of automating tasks typically performed by less experienced employees, including many routine and repetitive jobs [4, 5].”
From The Entry of Autistic University Graduates into the Labor Market in the Era of Generative AI: Their Concerns and Strategies Regarding the Technology. Research in Progress: The Entry of Autistic Graduates into the Labor Market in the Era of GenAI (2026) · cites this paper as [5]
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Generative AI for Software Practitioners
Ebert, C., & Louridas, P. (2023) · IEEE Software · doi:10.1109/ms.2023.3265877 · Cited by 293
Research works printed but not linked: 4
- O. Elazhary, "Investigation of the interplay between developers and automation," in Proc. 43rd Int. Conf. Software Engineering: Companion Proc. (ICSE '21), 2021, pp. 153-155. doi: 10.1109/MS.2023.3265877. · DOI · Paper page
- "Large Language Models as Tool Makers," arXiv, [Online]. Available: https://arxiv.org/pdf/2305.17126.pdf. · arXiv
- C. Bull and A. Kharrufa, "Generative AI assistants in software development education: a vision for integrating generative AI into educational practice, not instinctively defending against it," IEEE Software, 2023. doi: 10.1109/MS.2023.3300574. · DOI · Paper page
- J. Sauvola, S. Tarkoma, M. Klemettinen, J. Riekki, and D. Doermann, "Future of software develop- ment with generative AI," Received: 5 December 2023 / Accepted: 15 February 2024 / Published online: 11 March 2024. Available https://doi.org/10.1007/s10515-024-00426-z · DOI
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Matulewski, J., Sikorski, Ł., & Baczała, D. (2026) · Proceedings of the 63rd ACM Conference on Computers and People Research · doi:10.1145/3768310.3807803
Metadata
- Authors
- -, A.
- Venue
- International Journal For Multidisciplinary Research
- Published
- 2024-06-23
- Volume
- 6
- Issue
- 3
- ISSN
- 2582-2160 (ISSN-L)
- OpenAlex
- W4400172792
- Cited by
- 14
- References
- 1
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 1
- FWCI
- 3.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-sa
- Locations
- International Journal For Multidisciplinary Research (publishedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- generative AI, AI-assisted programming, large language models, GitHub Copilot, OpenAI Codex, code completion, automatic error detection, AI-generated code quality, ethical implications of AI, job displacement, software productivity
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