Development of Parallel Algorithms for Probabilistic Models Learning for Web Application Testing

Abstract

In the current conditions of testing methods and algorithms development to combine individual test components in the form of a hierarchical model reflecting the connections and states between these components, as well as allowing to evaluate the probability of a transition between model states in the event of information about the successful implementation of a certain test to detect a program error is of particular importance. Existing approaches do not allow optimal adjustment of model parameters, as well as qualitative calculation and establishment of directions of connections between individual components of this model. The scientific study considered the possibility of using effective numerical methods to solve the problem of training probabilistic models built on the basis of Bayesian networks to solve the main problems of testing web applications. The main approaches to create parallel algorithms implementing the main functional capabilities underlying the implementation of the procedure for training test models are considered. Analysis of the developed training algorithms effectiveness for testing certain groups of program errors allowing the most optimal parameters of the DBS model was made, as well as a justification for their use in distributed data processing systems. We have developed algorithmic solutions for Jacobi and Hesse matrices calculation optimization based on Cannon and Fox algorithms. The process of testing errors of web application is simulated and presented in the form of a dynamic Bayesian network obtained from the results of the structure and parameters learning procedure. The validity of all theoretical results is confirmed by a large number of experimental results proving the validity of the put forward assumptions, testing methods and models presented in the form of dynamic Bayesian networks.