A new approach for software fault prediction using feature selection
Majdi Mafarja, assistant professor at the Department of Computer Science at Birzeit University, along with two professors from Taif University and RMIT University, have developed a new approach for software fault predication (SFP), which addresses some of the limitations of existing machine learning SFP techniques. Their approach employs feature selection (FS) to enhance the performance of a layered recurrent neural network (L-RNN), which is used as a classification tool for SFP.
In their paper, published in Expert Systems with Applications, the research team proposed a novel FS approach to enhance the performance of a layered recurrent neural network (L-RNN) for SFP. The researchers employed three different wrapper FS algorithms iteratively: binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), and binary ant colony optimization (BACO).