Numerous logs generated during the daily deployment and operation process in cloud computing platforms help system administrators perform anomaly detection. Common anomalies in cloud workflow include pathway anomalies and time delay anomalies. Traditional anomaly detection methods train the learning models corresponding to the two kinds of anomaly detection tasks respectively and ignore the correlation between these two tasks, which leads to the decline of the accuracy of anomaly detection. In order to solve the problems, an anomaly detection method based on multi-task temporal convolutional network was proposed. Firstly, the event sequence and time sequence were generated based on the event templates of log stream. Then, the deep learning model based on the multi-task temporal convolutional network was trained. In the model, the event and the time characteristics were learnt in parallel from the normal system execution processes by sharing the shallow layers of the temporal convolutional network. Finally, the anomalies in the cloud computing workflow were analyzed, and the related anomaly detection logic was designed. Experimental results on the OpenStack dataset demonstrate that, the proposed method improves the anomaly detection accuracy at least by 7.7 percentage points compared to the state-of-art log anomaly detection algorithm DeepLog and the method based on Principal Component Analysis (PCA).

Stochastic Variational Inference (SVI) has been successfully applied to many types of models including topic models. Although it is extended to deal with large-scale data set with mapping the problem of reasoning to the optimization problems involving random gradient, the inherent noise of the stochastic gradient in SVI algorithm makes it produce large variance, which hinders fast convergence. In order to solve the problem, an improved Variance Reduced SVI (VR-SVI) was proposed. Firstly, the sliding window method was used to recalculate the noise term in the stochastic gradient, a new stochastic gradient was constructed, and the influence of noise on the stochastic gradient was reduced. Then, it was proved that the proposed algorithm could reduce the variance of random gradient on the basis of SVI. Finally, the influence of window size on the algorithm was discussed, and the convergence of algorithm was analyzed. The experimental results show that, the proposed VR-SVI algorithm can not only reduce the variance of stochastic gradient, but also save the computation time and achieve fast convergence.

In software testing, User Interface (UI) testing plays an important role to ensure software quality and reliability. Concerning the lack of stability and generality in the UI testing method for handle recognition, an improved method of recognizing and testing UI controls based on Extensible Markup Language (XML) was proposed through the introduction of XML. The method used the features of XML which processed data conveniently and combined the automation testing tool SilkTest to improve the traditional UI testing. Concerning the features of multi-language and multi-version in AutoCAD, the automation testing scheme was designed on the basis of the proposed method to test dialog box in a series of AutoCAD products. The experimental results show that the improved method reduces recognition time of the controls and the redundancy of the program. Also it improves the efficiency of the testing and the stability of UI controls recognition.