Prioritizing Coverage-Oriented Testing Process - An Adaptive-Learning-Based Approach and Case Study

This paper proposes a graph-model-based approach to prioritizing the test process. Tests are ranked according to their preference degrees which are determined indirectly, i.e., through classifying the events. To construct the groups of events, unsupervised neural network is trained by adaptive competitive learning algorithm. A case study demonstrates and validates the approach.