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Simulated Annealing and Genetic Algorithms for Optimal Regression Testing

✍ Scribed by Mansour, Nashat; El-Fakih, Khalid

Publisher
John Wiley and Sons
Year
1999
Tongue
English
Weight
252 KB
Volume
11
Category
Article
ISSN
1040-550X

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✦ Synopsis

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.

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