Natural optimization algorithms for optimal regression testing

Abstract

The optimal regression testing problem is that of determining the minimum number of test cases needed for revalidating modified software in the maintenance phase. The present two natural optimization algorithms, namely simulated annealing and genetic algorithms, 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 is that they do not suffer from exponential explosion for realistic program sizes. The experimental results show that they find optimal or near-optimal number of retests in a reasonable time.