Software fault classification using extreme learning machine: a cognitive approach

The software fault classification is very crucial in the development of reliable and high-quality software products. The fault classification allows determining and concentrating on fault software modules for early prediction of fault in time. As a result, it saves the time and money of the industry. Generally, various metrics are generated to represent the fault. But, selecting the dominant metrics from the available set is a challenge. Therefore, in this paper, a sequential forward search (SFS) with extreme learning machine (ELM) approach has used for fault classification. The number of features available in the metrics are selected to represent the fault using SFS and operated on ELM to verify the performance of software fault classification. Also, various activation functions of ELM have tested for the proposed work to identify the best model. The experimental result demonstrates that ELM with radial basis function achieves the good results compared to other activation function. Also, the proposed method has shown good results in comparison to support vector machine.