A kind of SAaGA Hybrid Meta-heuristic Algorithm for the Automatic Test Data Generation
Test data generation is very labor-intensive and expensive in software testing. The automation of test process can achieve significant reductions in the cost of software development. Combining the parallel search ability of the adaptive genetic algorithm (aGA) with the controllable jumping property of simulated annealing (SA), a kind of effective hybrid meta-heuristic algorithm (SAaGA) with the operators and parameters well designed is proposed for automatic test data generation in this paper. The experiments have shown that the SAaGA hybrid meta-heuristic algorithm may significantly reduce the cost and improve the percentage of code coverage compared to the genetic algorithm and simulated annealing