Simulated Annealing and Genetic Algorithms for Optimal Regression Testing
The optimal regression testing problem is one of determining the minimum number of test cases needed for revalidating modified software in the maintenance phase. We present two natural optimization algorithms, namely, a simulated annealing and a genetic algorithm, for solving this problem. The algor...
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1999
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| Online Access: | http://hdl.handle.net/10725/2968 http://dx.doi.org/10.1002/(SICI)1096-908X(199901/02)11:13.0.CO;2-M http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1096-908X(199901/02)11:1%3C19::AID-SMR182%3E3.0.CO;2-M/abstract |
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| Summary: | The optimal regression testing problem is one of determining the minimum number of test cases needed for revalidating modified software in the maintenance phase. We present two natural optimization algorithms, namely, a simulated annealing and a genetic algorithm, for solving this problem. The algorithms are based on an integer programming problem formulation and the program’s control flow graph. The main advantage of these algorithms, in comparison with exact algorithms, is that they do not suffer from an exponential explosion for realistic program sizes. The experimental results, which include a comparison with previous algorithms, show that the simulated annealing and genetic algorithms find the optimal or near-optimal number of retests within a reasonable time. Copyright 1999 John Wiley & Sons, Ltd. |
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