Advancements in AI Coding Assistance Tools and Their Potential Impact on Collaborative Software Development
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I2P101Keywords:
Artificial Intelligence, Coding Assistance Tools, Collaborative Software Development, Code Generation, Natural Language Processing, Github Copilot, Agile MethodologiesAbstract
The AI is soon to change software engineering because AI putpins such as GitHub Copilot, OpenAI Codex, TabNine, and Amazon CodeWhisperer are offering software developers more of a cushion than they have had before. They are streamlined to write code quicker, automate duties that require duplication, provide real-time bug detection and document creation with machine learning (ML) and natural language processing (NLP). Not only the productivity of one person can be altered, but team collaboration and team communication, minimizing knowledge siloes, and making distributed Agile practices can be radically enhanced by them. Throughout this paper, the author grants an in-depth discussion of the latest advances in the development of AI-aided code tools and how they could affect the collaborative software development processes. The research utilizes mixed research design where the review of the literature, methodological framework, and discussion of the experimental results can be applied to determine the technical capability and effects to organizations. The involvement of the state-of-the-art in AI code assistants in and before 2025, (2) a methodology to incorporate AI in collaborative workflow, (3) a performance, challenge, consideration analysis is important. We assume that AI assistance in the process of code creation can yield quantifiable features of productivity and quality improvement and that the issues of bias, intellectual property and overreliance on automation demonstrate that there is much more that can be done to alleviate these problems. As the results have shown, the appropriate implementation of the AI assistants in the working environment which is framed in terms of collaboration requires the hybrid human-AI workflows, the strong models of governance, and the flexible system of software development
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