Automated test data generation for branch testing using genetic algorithm: An improved approach using branch ordering, memory and elitism

One of the problems faced in generating test data for branch coverage using a metaheuristic technique is that the population may not contain any individual that encodes test data for which the execution reaches the predicate node of the target branch. In order to deal with this problem, in this paper, we (a) introduce three approaches for ordering branches for selection as targets for coverage with a genetic algorithm (GA) and (b) experimentally evaluate branch ordering together with elitism and memory to improve test data generation performance. An extensive preliminary study was carried out to help frame the research questions and fine tune GA parameters which were then used in the final experimental study.

► We consider search based test data generation for branch coverage.
► The population may contain no individual for which the execution path reaches the predicate node of the target branch.
► We consider branch ordering for selecting branches as targets, elitism and memory to improve test data generation performance.
► A preliminary study is carried out to frame research questions and fine tune GA parameters.
► Experiments indicate that elitism, memory and branch ordering may significantly improve performance.