Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

Abstract

In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FADE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm's performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.

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“…In traditional DE algorithms, the search process consists of three single loops, and the fitness function value of each individual in the population is calculated twice. According to work [35], [39], the time complexity ( ) O DE of the DE algorithm is defined as follows:…”