A Mathematical Model for Ranking High-Level User Interface Regression Tests

Abstract

A mathematical model has been developed for ranking high-level verification scenarios (manual tests) when forming a pool of regression tests. The model takes into account the significance of selection methods based on the analysis of previous runs of regression tests, the opinion of the expert group, the specifics of the changes made in the current release, and also has the ability to use an arbitrary number of additional test selection methods. The model can be used to analyze historical data of regression test runs using neural networks in order to identify the most effective approaches to selecting tests for regression testing. The model can be implemented in a software package that interacts with various testing management systems in order to significantly accelerate the formation of a pool of regression tests with a different approach to selection, which can be used by a test engineer of average qualification and without a deep understanding of the features and architecture of the information system being developed.