Evolutionary Multi-Objective Optimization for Data-Flow Testing of Object-Oriented Programs

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

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.

Keywords

Evolutionary Algorithm multi-objective path coverage CBEGA immigration rate 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Information TechnologyPondicherry Engineering CollegePuducherryIndia

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