Probabilistic approaches development for optimizing learning parameters procedure for testing models via the expectation-maximization method
Year: 2022
Type: article
Abstract: The development and improvement of training algorithms is of special scientific and practical interest in the field of application testing. In the context of expanding the field of application of probabilistic models of Bayesian networks for solving testing problems, there is a need for stochastic assessment of hidden parameters, as well as the formation of a complete joint distribution across all variables. The growth of the dimension of probabilistic models for solving various scientific and applied problems requires the development of optimal procedures for training parameters in accordance with existing algorithms, as well as the development of new solutions. The subject of the study is testing models used to represent probabilistic relationships between individual test modules, which allow timely configuration and use of test generation to detect certain groups of program errors. The aim of the study is to develop effective stochastic algorithms for training parameters of test models, which allow you to obtain more accurate values of a priori distribution of parameters. The study proposes methods and algorithms for training parameters of testing models based on a stochastic gradient in combination with the Monte Carlo method using Markov chains to form a preliminary distribution over all hidden variables in accordance with the available set of training data. The results of the study showed the viability of theoretical assumptions and made it possible to implement the optimal algorithm for solving the problem of training probabilistic models of testing web applications. The proposed methods and algorithms are constructive and make it possible to increase the accuracy of training parameters of test models in the presence of hidden variables.
Author: Pavel V. Polukhin
Institution: Voronezh State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4290342811