Model Driven Approach to Secure Optimized Test Paths for Smart Samsung Pay using Hybrid Genetic Tabu Search Algorithm

Smart mobile pay applications on smart devices have been considered as the most efficient and secure mode of contactless payment. To safeguard customer credit/ debit card details, testing of mobile pay solutions like Samsung Pay is most important and critical task for testers. Testing of all the test cases is very tedious and a time-consuming task, therefore optimization techniques have been used to identify most optimized test paths. In this article, a hybrid genetic and tabu search optimization (HGTO) algorithm is proposed to secure optimized test paths using activity diagram of the smart Samsung Pay application. The proposed approach has been implemented using C++ language on the case study of the Smart Samsung Pay and an online airline reservation system. The experimental results show that the proposed technique is more effective in automatic generation and optimization of test paths, as compared to a simple genetic algorithm.