Automatic test data generation based on multi-objective ant lion optimization algorithm
Mayank Singh, Viranjay Mohan Srivastava, Kumar Gaurav, P. K. Gupta · 2017
Automated software testing allows testers and managers for generating the quality of test data during each phase of software development. Path coverage based testing is the most effective technique in structural testing. The major challenge in path coverage based testing is to generate the test data to cover complete path from beginning till end. Therefore, we require a novel automated test data generation method for the same. Various soft computing techniques are being used to generate the path coverage tests for search-based software testing. In this paper, we have focused on resolving the multi-objective optimization of coverage based test data by proposing Multi-Objective Ant Lion Optimization (MOALO) algorithm. Further, we have discussed that how the proposed algorithm enhance the path coverage with reduced number of tests. To validate the proposed algorithm, we have compared the obtained experimental results with random resting and conventional genetic algorithm's data. These results shows that proposed algorithm outperforms the existing algorithms.