UMA REVISÃO SISTEMÁTICA SOBRE O USO DE INTELIGÊNCIA ARTIFICIAL NA GERAÇÃO AUTOMÁTICA DE CASOS DE TESTE DE SOFTWARE

This article presents a systematic review on the use of Artificial Intelligence (AI) in the automated generation of software test cases, covering the main techniques, tools, challenges, and future perspectives. The research was conducted through searches in recognized academic databases, considering publications from 2019 to 2025. The methodological process included keyword definition, rigorous article selection, and critical analysis of existing approaches, encompassing evolutionary algorithms, machine learning, natural language processing, and multi-objective optimization. The survey addresses key questions: What are the predominant techniques for automating test case generation? How does AI contribute to increasing test coverage and efficiency? What are the main challenges for adopting these solutions in production environments? The results indicate that combining traditional methods with artificial intelligence enhances test quality and adaptability, although challenges related to explainability, structural coverage, and integration remain. This work provides a solid foundation for future research and practical applications in computer engineering.