ArticleOpen Access http://dx.doi.org/10.26855/ftair.2024.12.007
Research on Automated Software Development Processes Based on Artificial Intelligence Algorithms
Yue Hou*, Jing Liu
Meiga Polytechnic Institute of Hubei, Xiaogan 432017, Hubei, China.
*Corresponding author: Yue Hou
Published: November 28,2024
Abstract
Traditional software development methods have many problems such as high price, low efficiency, and difficult quality assurance. It is urgent to introduce artificial intelligence algorithms to make changes and promote its automation transformation. Based on this, this article starts from the perspective of the entire software development life cycle, including requirements analysis, design, coding, testing and maintenance, and explains how to use artificial intelligence algorithms to improve the automation process of software development, aiming to provide reference for the intelligent transformation of the software development industry. AI (Artificial Intelligence) software development focuses on creating applications that can perform tasks requiring human-like intelligence, such as learning, reasoning, and decision-making. AI development requires interdisciplinary knowledge in computer science, mathematics, and domain-specific expertise. The field continues to evolve rapidly, driving innovation across industries. AI software development involves designing, building, and deploying applications that mimic human intelligence—enabling machines to learn, reason, and make decisions. This field combines computer science, data science, and domain expertise to create systems that improve over time.
Keywords
Artificial intelligence algorithm; software development; automated process; software life cycle
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How to cite this paper
Research on Automated Software Development Processes Based on Artificial Intelligence Algorithms
How to cite this paper: Yue Hou, Jing Liu. (2024) Research on Automated Software Development Processes Based on Artificial Intelligence Algorithms. Future Trends in AI Research, 1(1), 27-33.
DOI: http://dx.doi.org/10.26855/ftair.2024.12.007