Software defect prediction is an important part of the software testing field. According to the characteristics of object-oriented software, this paper considers the evolution information separately in different packages and summarizes the evolution metrics that affect the defect prediction. Existing research on evolutionary information often ignores the impact of newly added and disappearing classes on software defects prediction. Based on these factors, evolution metrics are proposed and applied to defect prediction. Two evolution metrics, transition class ratio and static metric category number, are proposed for object-oriented cross-version defect prediction. Experiments are carried out in the commonly used software defect prediction set. The experimental results show that the proposed metrics have better defect prediction ability than the traditional static metric.

In the cloud computing system environment, combined with the first-level scheduling model of task-virtual machine resource nodes, the individual coding, fitness function, selection replication and cross-variation process are redesigned, and the cloud computing resource scheduling model based on genetic algorithm is established. Corresponding to fireflies and virtual machine resource nodes, this paper redesigned the firefly decision domain update method, selected attraction probability formula and location movement strategy, and combined with genetic algorithm to establish cloud computing resource scheduling model based on firefly-genetic algorithm. Experiment with the CloudSim cloud computing simulation platform. The results show that the task completion time of the resource scheduling model is smaller than that of the single genetic algorithm. The virtual machine load is more balanced, the task completion time is short, and the overall optimization effect of the resource scheduling scheme is obvious.

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