Model-Based Test Prioritizing - A Comparative Soft-Computing Approach and Case Studies

Abstract

Man-machine systems have many features that are to be considered simultaneously. Their validation often leads to a large number of tests; due to time and cost constraints they cannot exhaustively be run. It is then essential to prioritize the test subsets in accordance with their importance for relevant features. This paper applies soft-computing techniques to the prioritizing problem and proposes a graph model-based approach where preference degrees are indirectly determined. Events, which imply the relevant system behavior, are classified, and test cases are Clustered using (i) unsupervised neural network clustering, and (ii) Fuzzy c-Means clustering algorithm. Two industrial case studies validate the approach and compare the applied techniques.