Abstract
In search based test data generation, the problem of test data generation is reduced to that of function minimization or maximization.Traditionally, for branch testing, the problem of test data generation has been formulated as a minimization problem. In this paper we define an alternate maximization formulation and experimentally compare it with the minimization formulation. We use genetic algorithm and binary particle swarm optimization as the search technique and in addition to the usual operators we also employ a branch ordering strategy, memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the branch ordering strategy, memory and elitism.
Highlights
Search-based software test data generation has emerged [1, 2, 3, 4, 5, 6] as a significant area of research in software engineering
For branch testing, the problem of test data generation has been formulated as a minimization problem
ANOVA was carried out using SYSTAT 9.0 to determine significant difference in means for experiments with Genetic Algorithm only
Summary
Search-based software test data generation has emerged [1, 2, 3, 4, 5, 6] as a significant area of research in software engineering. A test set T is said to satisfy the branch coverage criterion if on executing P on T, every branch in P’s flow graph is traversed at least once Metaheuristic techniques such as genetic algorithms [9], quantum particle swarm optimization [10], scatter search [11] and others have been applied to the problem of automated test data generation and provide evidence of their successful application. Amongst these several have addressed the issue of test data generation with program-based criteria [10] and in particular the branch coverage criterion [10, 11, 12, 13, 14, 15, 16, 17, 18].
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