Failure Analysis of Static Analysis Software Module Based on Big Data Tendency Prediction - Jian Zhu; Qian Li; Shi Ying (Complexity)
With the continuous development of software, it is inevitable that there will be various unpredictable problems in computer software or programs that will damage the normal operation of the software. In the paper, static analysis software is taken as the research object, the errors or failures caused by the potential defects of the software modules are analyzed, and a software analysis method based on big data tendency prediction is proposed to use the software defects of the stacked noise reduction sparse analyzer to predict. This method can learn features from original defect data, directly and efficiently extract required features of all levels from software defect data by setting different number of hidden layers, sparse regularization parameters, and noise ratio, and then classify and predict the extracted features by combining with big data. Through experimental tests, the performance of the presented method is better than that of the comparison method in correct rate, accuracy rate, recall rate, F1-measurement, AUC value, and running time, which proves that the research results in this paper have more accurate failure prediction effect and can timely eliminate software failures.