Approach to Multi-path Coverage Testing Based on Path Similarity Table and Individual Migration

Abstract

The application of genetic algorithm in multi-path coverage testing is a research hotspot. In the process of iteration between the old and new populations, the old population may contain excellent individuals from other sub-populations, which are not fully utilized, resulting in resource waste. At the same time, the number of individuals in the population will be much greater than that of reachable paths, and each individual will go through a reachable path. This causes multiple individuals to pass through the same path, leading to repeated calculation of the similarity between the individual and the target path. Based on this, a multi-path coverage testing method combined with path similarity table and individual migration is proposed to improve testing efficiency. By storing the calculated path similarity value in the path similarity table, the value can be avoided from being calculated repeatedly and the testing time can be reduced. In the evolutionary process, the individual path is compared with other target paths, and if the similarity reaches the threshold, the excellent individual is migrated to the sub-population corresponding to the path, which improves the utilization rate of individuals and reduces the evolutionary generation. Experiments show that, compared with other six classic methods, the proposed method reduces the average generation time on eight programs by up to 44.64%, and the minimum is 2.64%, and the average evolution generation is reduced by up to 35.08%, and the minimum is 6.13%. Therefore, the proposed method effectively improves the test efficiency.

Keywords

• test case; path similarity table; individual migration; multi-path coverage; multi-population genetic algorithm