Abstract

The process of bug identification is complicated and time-consuming. To effectively reconstruct, localize and solve issues need to select the appropriate tools, techniques and methods. To solve this issue, we propose the cloud security in the financial application by developing an innovative bug identifying method with the use of artificial intelligence (AI) called Shuffled Frog Leaping-driven Dynamic One-class Support Vector Machine (SFL-DOSVM). We gather a dataset and pre-process the obtained raw data by utilizing the text pre-processing techniques that eliminates the redundant data and improves the data quality. Term Frequency-Inverse Document Frequency (TF-IDF) method is employed to extract the significant features. Accuracy, recall, MCC, f1-measure and precision are the metrics used in the bug identification approaches. Performance evaluation carried out by the comparison of proposed and traditional techniques such as Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The efficient outcomes are demonstrated by our proposed method than other existing techniques.