An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing
Authors: 
José Carlos B. Ribeiro, Mário Alberto Zenha-Rela, Francisco Fernández de VegaAuthors Info & Claims
José Carlos B. Ribeiro, Mário Alberto Zenha-Rela, Francisco Fernández de VegaAuthors Info & ClaimsPages 1949 - 1950
Abstract
This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while introducing a negligible computational overhead.
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P. J. Angeline. Adaptive and self--adaptive evolutionary computations. In Computational Intelligence: A Dynamic Systems Perspective, pages 152--163. IEEE Press, 1995.
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A. E. Eiben, R. Hinterding, and Z. Michalewicz. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3:124--141, 1999.
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S. Luke. Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation, 4(3):274--283, Sept. 2000.
[4]
J. C. B. Ribeiro. Search--based test case generation for object-oriented java software using strongly-typed genetic programming. In GECCO '08: Proceedings of the 2008 GECCO Conference Companion on Genetic and Evolutionary Computation, pages 1819--1822, New York, NY, USA, 7 2008. ACM.
[5]
J. C. B. Ribeiro, M. Z. Rela, and F. F. de Vega. A strategy for evaluating feasible and unfeasible test cases for the evolutionary testing of object-oriented software. In AST '08: Proceedings of the 3rd International Workshop on Automation of Software Test, pages 85--92, New York, NY, USA, 2008. ACM.
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- An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing
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Published: 08 July 2009
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