A Genetic Algorithm-based System for Automatic Control of Test Data Generation

Paul POCATILU, Ion IVAN
Bucharest University of Economic Studies
Piata Romana 6, Bucharest, Romania
ppaul@ase.ro, ionivan@ase.ro

Abstract: Software testing is an important process that helps to develop high quality software. This process is more time consuming when applied to control systems. This involves the use of several testing strategies, techniques and methodologies. At the module level one of test technique is to assure as much as possible code coverage. This is accomplished using several methods, one of them being automatic test data generation. Test data generation can be done manually, randomly or using combinatorial optimizing techniques. Another technique involves the use of genetic algorithm (GA). The paper presents a system that involves an automatic control of test data generation. It also provides implementation details of a test data generator (TDG) based on GA that uses a specific fitness function called Inverse Similarity of Coverage (ISC). The test data generator is a module of the proposed system. The results show that the proposed solution, GA-TDG, has far better results in many relevant situations than random test generators regarding the number of software under test (SUT) runs.

Keywords: Software testing process, control system, genetic algorithm, test data generation, fitness function, code coverage, random data generation, control decision.

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CITE THIS PAPER AS:
Paul POCATILU, Ion IVAN, A Genetic Algorithm-based System for Automatic Control of Test Data Generation, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (2), pp. 219-226, 2013. https://doi.org/10.24846/v22i2y201312