Modeling User Behavior with Markov Chains for Optimizing Regression Testing Paths in Multi-Module Software Products

Khudenko Daniil

Citation: Khudenko Daniil, "Modeling User Behavior with Markov Chains for Optimizing Regression Testing Paths in Multi-Module Software Products", Universal Library of Engineering Technology, Volume 01, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Due to the rapid increase in complexity of multi-module software systems, classical regression testing methods prove ineffective: they produce a combinatorial explosion of test scenarios and consume resources disproportionate to the value of the results. This study aims to overcome these challenges by constructing models of user behavior. The objective is to substantiate the theoretical premises and describe a model for optimizing regression testing paths based on Markov chains. The methodological basis of the study includes analysis and synthesis of contemporary research in software engineering and machine learning. As a result, a conceptual framework is formed that makes it possible to identify and rank the most frequently occurring and critically significant sequences of interactions among the system modules. This provides the foundation for creating a targeted set of regression tests focused on scenarios with the highest probability of defect occurrence. Analysis of the obtained data demonstrates the possibility of reducing the effort required for regression testing while maintaining a high level of coverage of key functionality. The materials presented will be of interest to quality assurance specialists, IT project managers, and other researchers engaged in the automation and optimization of software testing processes.


Keywords: Markov Chains; Prioritization of Test Scenarios; Regression Testing; Software Engineering; Software Quality; Testing Optimization; User Behavior.

Download doi https://doi.org/10.70315/uloap.ulete.2024.0102012