arXiv:2607.00558v1 Announce Type: cross Abstract: As Artificial Intelligence(AI)-based applications take off, a clear understanding of AI patterns can uplift the quality of AI applications. Many AI patterns have been proposed in the literature; however, their prevalence in real-life code has not yet been validated. Understanding the actual use of those patterns in practice can clarify our understanding both of the significance of these patterns and their utility. In this paper, we present a methodology to a) identify relevant patterns by mining the literature and then to b) validate their pres
The proliferation of AI-based applications necessitates a more rigorous understanding and validation of AI patterns to ensure quality and reliability in real-world deployments.
A validated methodology for identifying AI pattern prevalence can significantly uplift the quality and predictability of AI applications, driving broader adoption and trust in AI systems.
The ability to systematically understand and deploy proven AI patterns will improve development practices, moving AI application development towards more standardized and robust engineering disciplines.
- · AI software developers
- · Software engineering researchers
- · Enterprises adopting AI
- · Ad-hoc AI development
- · Low-quality AI applications
Improved quality and reliability of AI applications.
Faster development cycles and reduced costs for AI-driven products and services due to pattern reuse.
Enhanced overall AI ecosystem maturity, fostering innovation and wider economic impact from AI technologies.
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Read at arXiv cs.AI