SOFTWARE TESTING IN A SILICON BOX: A NEW APPROACH BASED ON ARTIFICIAL INTELLIGENCE

Abstract

The increasing complexity of software systems and the massive volume of data pose significant challenges to traditional testing methods, requiring more agile and efficient approaches. This paper proposes the “Silicon Box”, a new software testing system based on artificial intelligence (AI), which aims to overcome the limitations of conventional testing techniques (black, white and gray box) through intelligent and adaptive automation. The main methodology of this work was the literature review and theoretical foundation, based on studies and methods previously developed to support and strengthen the method proposed by this paper. The system functions as a requirements specification analyzer, an intelligent AI-driven test case generator for scenario creation, and a results analyzer with looped feedback for continuous system learning. The expected advantages include a substantial reduction in manual effort, a large increase in test coverage, and early identification of defects, thus promoting continuous improvement in resource allocation. The “Silicon Box” represents a practical evolution in software testing strategies, unifying computational reasoning with autonomous execution.