International Journal For Multidisciplinary Research

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Generative Ai in Software Development : an Overview and Evaluation of Modern Coding Tools

Author(s) Aarti
Country India
Abstract 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.
Keywords Generative AI, software development, large language models, ethical AI, contextual understanding, human-AI collaboration.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-23
Cite This Generative Ai in Software Development : an Overview and Evaluation of Modern Coding Tools - Aarti - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.23271
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.23271
Short DOI https://doi.org/gt243h
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