Abstract

The fundamental purpose of software testing is to develop new test case sets that demonstrate the software product's deficiencies. Upon preparation of the test cases, the Test Oracle delineates the expected program behavior for each scenario. The application's correct functioning and its properties will be assessed by prioritizing test cases and running its components, which delineate inputs, actions, and outputs. The prioritization methods include initial ordering, random ordering, and reverse ranking based on fault detection capabilities. software application development often used a test suite, which was less well recognized as a suite for validating software correctness. Each test case set in the suite had distinct instructions and goals based on the system and its configuration that were evaluated. This article presents a Generative AI artificial neural network model for automated software testing based on particle swarm optimization. Generative AI is the method by which computers use existing data, including text, audio/video files, images, and code, to produce new material. An artificial neural network (ANN) is a complex adaptive system capable of altering its internal structure in response to the information it processes. Precisely manipulate the connection and its weight to achieve optimal accuracy. The PSO is a heuristic, population-based global optimization method.