Model-based test case prioritization using cluster analysis: a soft-computing approach

Authors : NİDA GÖKÇE, FEVZİ BELLİ, MÜBARİZ EMİNLİ, BEKİR TANER DİNÇER

Pages : 623-0

Doi:10.3906/elk-1209-109

View : 14 | Download : 7

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Model-based testing is related to the particular relevant features of the software under test insert ignore into journalissuearticles values(SUT); and its environment. Real-life systems often require a large number of tests, which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance as the tester perceives it, usually given by several attributes of relevant events entailed. Based on event-oriented graph models, this paper proposes an approach to ranking test cases in accordance with their preference degrees. For forming preference groups, events are clustered using an unsupervised neural network and fuzzy c-means clustering algorithm. The suggested approach is model-based, so it does not necessitate the availability of the source code of the SUT. It differs from existing approaches also in that it needs no prior information about the tests carried out before. Thus, it can be used to reflect the tester`s preferences not only for regression testing as is common in the literature but also for ranking test cases in any stage of software development. For the purpose of experimental evaluation, we compare the suggested prioritization approach with six well-known prioritization methods.
Keywords : Test prioritization, model based testing, event oriented graphs, event sequence graphs, clustering algorithms, fuzzy c means, neural networks