Research on Performance Test of Chinese Character APP Software Based on OL-ADE Algorithm

Entering the information age, Chinese character APP assumes the task of cultural dissemination in a novel form as a carrier. And, software performance testing, a means to ensure software quality, is needed to maintain its performance. In view of this, the research first designs an opposition-based learning of adaptive evolution based on reverse learning strategy (OL-ADE), and then builds a Chinese character APP software performance test model on this basis and put it into application, and finally verify its effect through experiments. The results show that when the population size is 50 or 100, the OL-ADE algorithm has the least number of iterations under different data input ranges, and the number of iterations is within [40, 150]. The branch coverage is 100%, and the average number of iterations required by OL-ADE algorithm is the smallest. The above results show that OL-ADE algorithm has the best overall performance among the four algorithms, and can complete the software performance test of Chinese character APP.