Adaptive Deep Learning Model for Software Bug Detection and Classification

Abstract

Software is unavoidable in software development and maintenance. In literature, many methods are discussed which fails to achieve efficient software bug detection and classification. In this paper, efficient Adaptive Deep Learning Model (ADLM) is developed for automatic duplicate bug report detection and classification process. The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory (CRF-LSTM) and Dingo Optimizer (DO). In the CRF, the DO can be consumed to choose the efficient weight value in network. The proposed automatic bug report detection is proceeding with three stages like pre-processing, feature extraction in addition bug detection with classification. Initially, the bug report input dataset is gathered from the online source system. In the pre-processing phase, the unwanted information from the input data are removed by using cleaning text, convert data types and null value replacement. The pre-processed data is sent into the feature extraction phase. In the feature extraction phase, the four types of feature extraction method are utilized such as contextual, categorical, temporal and textual. Finally, the features are sent to the proposed ADLM for automatic duplication bug report detection and classification. The proposed methodology is proceeding with two phases such as training and testing phases. Based on the working process, the bugs are detected and classified from the input data. The projected technique is assessed by analyzing performance metrics such as accuracy, precision, Recall, F_Measure and kappa.