Research Article
A Comparative Analysis of AI System Development Tools for Improved Outcomes
Ikechukwu Innocent Umeh*
,
Kobimdi Cordelia Umeh
Issue:
Volume 11, Issue 1, June 2025
Pages:
1-20
Received:
12 October 2024
Accepted:
8 November 2024
Published:
17 January 2025
DOI:
10.11648/j.ijsmit.20251101.11
Downloads:
Views:
Abstract: This study presents a comparative analysis of various artificial intelligence (AI) system development tools, emphasizing their effectiveness in enhancing software engineering outcomes. As AI technologies continue to evolve, tools designed for their development have become pivotal in optimizing processes, such as code generation, testing, and deployment. This research systematically evaluates prominent AI tools such as TensorFlow, PyTorch, and GitHub Copilot based on standardized criteria including usability, scalability, performance, and integration capabilities. This study also examines the impact of selected AI tools on collaborative development practices and team dynamics within software projects. Furthermore, the study explored the ethical considerations and potential biases inherent in AI-assisted development, emphasizing the importance of responsible tool selection and usage. The findings indicated that the selection of appropriate AI tools can significantly impact productivity, code quality, and project success. By identifying the strengths and limitations of these tools, this study provides valuable insights for practitioners, aiding them in making informed decisions that align with specific project requirements. Additionally, the analysis highlights gaps in the current landscape of AI development tools and suggests future research directions for fostering innovation in this critical area of software engineering. The findings underscore the need for ongoing education and training for developers to effectively leverage evolving AI technologies in their workflows.
Abstract: This study presents a comparative analysis of various artificial intelligence (AI) system development tools, emphasizing their effectiveness in enhancing software engineering outcomes. As AI technologies continue to evolve, tools designed for their development have become pivotal in optimizing processes, such as code generation, testing, and deploy...
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