Abstract
This paper presents a Class-Based Elitist Genetic Algorithm (CBEGA) to generate a suite of tests for testing the object-oriented programs using evolutionary multi-objective optimization techniques. Evolutionary Algorithms (EAs) are inspired by mechanisms in biological evolution like reproduction, mutation, recombination, and selection. EA applies these mechanisms repeatedly to a set of individuals called population to obtain solution. Multi-objective optimization involves optimizing a number of objectives simultaneously. The objectives considered in this paper for optimization are maximum coverage, minimum execution time and test-suite minimization. The experiment shows that CBEGA gives 92% path coverage and simple GA gives 88% path coverage for a set of java classes.
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Maragathavalli, P., Kanmani, S. (2013). Evolutionary Multi-Objective Optimization for Data-Flow Testing of Object-Oriented Programs. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_28
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DOI: https://doi.org/10.1007/978-3-642-31552-7_28
Publisher Name: Springer, Berlin, Heidelberg
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