In software engineering, defect prediction is significantly important and challenging. The main task is to predict the defect proneness of the modules. It helps developers find bugs effectively and prioritize their testing efforts. At present, a lot of valuable researches have been done on this topic. However, few studies take into account the impact of time factors on the prediction results. Therefore, in this paper, we propose an improved Elman neural network model to enhance the adaptability of the defect prediction model to the time-varying characteristics. Specifically, we optimized the initial weights and thresholds of the Elman neural network by incorporating adaptive step size in the Cuckoo Search (CS) algorithm. We evaluated the proposed model on 7 projects collected from public PROMISE repositories. The results suggest that the contribution of the improved CS algorithm to Elman neural network model is prominent, and the prediction performance of our method is better than that of 5 baselines in terms of F-measure and Cliff’s Delta values. The F-measure values are generally increased with a maximum growth rate of 49.5% for the POI project.

Behavioral plasticity is of great significance because it allows individuals to reversibly respond to variations in the ecological and social environment to increase their fitness (Bretman et al., 2011).Early exposure to new ornamentations in fruit flies (Verzijden et al., 2015) or butterflies (Westerman et al., 2012) led to shifts in mate preferences in sexually mature older individuals. Many other studies have reported that prior experience influences mate choice

It was reported that temperature affects the distribution of Wolbachia in the host, but only a few papers reported the effect of the interaction between high temperature and Wolbachia on the biological characteristic of the host. Here, we set four treatment Drosophila melanogaster groups: Wolbachia-infected flies in 25 °C (W+M), Wolbachia-infected flies in 31 °C (W+H), Wolbachia-uninfected flies in 25 °C (W-M), Wolbachia-uninfected flies in 31 °C (W-H), and detected the interaction effect of temperature and Wolbachia infection on the biological characteristic of D. melanogaster in F1, F2 and F3 generations. We found that both temperature and Wolbachia infection had significant effects on the development and survival rate of D. melanogaster. High temperature and Wolbachia infection had interaction effect on hatching rate, developmental durations, emergence rate, body weight and body length of F1, F2 and F3 flies, and the interaction effect also existed on oviposition amount of F3 flies, and on pupation rate of F2 and F3 flies. High temperature stress reduced the Wolbachia vertical transmission efficiency between generations. These results indicated that high temperature stress and Wolbachia infection had negative effects on the morphological development of D. melanogaster.