Using Artificial Bee Colony Algorithm for MLP Training on Software Defect Prediction

Defects in software systems continue to be a major problem. Defect prediction is an important topic in software quality research and could help on planning, controlling and executing software development activities. Nowadays, computer scientists have shown the interest in the study of social insect’s behaviour in neural networks area for solving different prediction problems.Chief among these is the Artificial Bee Colony (ABC) algorithm. This paper investigates the use of ABC algorithm that simulates the intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron (MLP) trained with the standard back propagation algorithm normally utilises computationally intensive training algorithms. One of the crucial problems with the backpropagation (BP) algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome ABC algorithm used in this work to train MLP learning the complex behaviour of software defect prediction data trained by BP, the performance of MLP-ABC is benchmarked against MLP training with the standard BP. The experimental result shows that MLP-ABC performance is better than MLP-BP.