Title: Automatic Test Suit generation with Genetic Algorithm
Abstract:Software testing is most effort consuming phase in software development. One would like to minimize the efforts and maximize the number of faults detected. Hence test case generation may be treated as...Software testing is most effort consuming phase in software development. One would like to minimize the efforts and maximize the number of faults detected. Hence test case generation may be treated as an optimization problem. One of the major difficulties in software testing is the automatic generation of test data that satisfy a given adequacy criterion. Generating test cases automatically will reduce cost and efforts significantly. In this paper, test case data is generated automatically using Genetic Algorithms and results are compared with Random Testing. It is observed that Genetic Algorithms outperforms Random Testing. Keywords: Automatic test data generation, Equivalence Class Partitioning, Evolutionary algorithms, Random Testing, Software testing. I. Introduction Software development consists of various phases like Requirement analysis, Design, Coding and Testing. Out of these testing consumes maximum efforts. The software is tested with enough set of test cases, to make a judgment about quality or acceptability and to discover errors. Two fundamental techniques used to identify test cases are functional and structural testing (1). Testing of software using these two approaches is very time consuming. So to reduce the efforts there should be a mechanism to generate test cases automatically. A number of techniques are available to generate automatic test cases like random testing, anti-random testing etc. Objective of all these techniques is to find minimal number of test cases to test the software fully. This can be considered as an optimization problem. To solve optimization problems there are a number of techniques and one of them is Genetic Algorithms. Genetic algorithms are population based search based on the Darwin's principle of survival of the fittest. GA is basically an evolutionary technique inspired by biological evolution. It was developed in 1970's by J. Holland and his colleagues and his students at University of Michigan's (2), (3). It mimics the process of natural evolution. GA starts with a initial population and then apply genetic operators like selection, crossover, mutation and replacement on that population to evolve better and better individuals. GA can be terminated in either of two cases: maximum number of generations achieved or optimum value found.Read More
Publication Year: 2013
Publication Date: 2013-01-01
Language: en
Type: article
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Cited By Count: 2
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