Grouping target paths for evolutionary generation of test data in parallel

Generating test data covering multiple paths using multi-population parallel genetic algorithms is a considerable important method. The premise on which the method above is efficient is appropriately grouping target paths. Effective methods of grouping target paths, however, have been absent up to date. The problem of grouping target paths for generation of test data covering multiple paths is investigated, and a novel method of grouping target paths is presented. In this method, target paths are divided into several groups according to calculation resources available and similarities among target paths, making a small difference in the number of target paths belonging to different groups, and a great similarity among target paths in the same group. After grouping these target paths, a mathematical model is built for parallel generation of test data covering multiple paths, and a multi-population genetic algorithm is adopted to solve the model above. The proposed method is applied to several benchmark or industrial programs, and compared with a previous method. The experimental results show that the proposed method can make full use of calculation resources on the premise of meeting the requirement of path coverage, improving the efficiency of generating test data.

► We present a method of grouping target paths.
► Target paths are evenly divided and paths in the same group have a great similarity.
► We build a mathematical model for parallel generation of test data.
► The method enhances resource utility.